Estimating the optimal interval between rounds of indoor residual spraying of insecticide using malaria incidence data from cohort studies.

BACKGROUND: Indoor residual spraying (IRS) reduces vector densities and malaria transmission, however, the most effective spraying intervals for IRS have not been well established. We estimated the optimal timing interval for IRS using a statistical approach.
METHODS: Six rounds of IRS were implemented in Tororo District, a historically high malaria transmission setting in Uganda, during the study period (3 rounds with bendiocarb active ingredient (Ficam®): December 2014 to December 2015, and 3 rounds with pirimiphos methyl active ingredient (Actellic 300®CS): June 2016 to July 2018). A generalized additive model was used to estimate the optimal timing interval for IRS based on the predicted malaria incidence. The model was fitted to clinical incidence data from a cohort of children aged 0.5-10 years from selected households observed throughout the study period.
RESULTS: 494 children, 67% aged less than 5 years at enrolment were analysed. Six-months period incidence of malaria decreased from 2.96 per person-years at the baseline to 1.74 following the first round of IRS and then to 0.02 after 6 rounds of IRS. The optimal time interval for IRS differed between bendiocarb and pirimiphos methyl and by IRS round. To retain an optimum impact, bendiocarb would require respraying 17 (95% CI: 14.2-21.0) weeks after application whereas pirimiphos methyl could remain impactful for 40 (95% CI: 37.0-42.8) weeks, although in the final year this estimates 36 (95% CI: 32.7-37.7) weeks. However, we could not estimate from the data the optimal time after the second and third rounds of bendiocarb and after the second round of pirimiphos methyl. Neither the amount of rainfall nor the EIR nor the distribution of nets were found to be statistically significant for determining the time period between spray rounds.
CONCLUSION: In our setting, the effect of the two IRS products was distinct. Statistically, pirimiphos methyl provided a longer window of protection than bendiocarb, although impact varied between different spray rounds and years which was not explained by rainfall or EIR or distribution of nets in our statistical approach. Understanding the effectiveness of IRS and how long it lasts can help for planning campaigns, but one should consider the financial cost and insecticide resistance. Monitoring the timing of spray campaigns using clinical incidence could be repeated in future programs to help determine the average period of protectivity of these products.

Introduction

Despite recent efforts to scale-up coverage of malaria control interventions and the renewed focus on elimination, malaria remains a major global health problem [1]. However, marked declines in malaria burden have been documented in many sub-Saharan African counties linked to scale-up of vector control interventions including the use of insecticide treated bed-nets and indoor residual spraying (IRS) [1, 2]. Studies of malarial control in sub-Saharan African countries show that IRS, dramatically reduces malaria and its vectors [2, 3]. Similar to other African countries, IRS in Uganda has been shown to reduce the risk of parasitemia, anemia, malaria morbidity, vector densities and malaria transmission [4-7]. However, some IRS products have contrasting impacts [8]. For example, a study in Benin, West Africa shows that short lasting efficacy of Actellic 50 EC may render IRS ineffective [9].

The effectiveness of IRS may be affected by a number of factors including; the nature of the insecticide used, operational factors, malaria endemicity, and environmental factors [8]. The other factor which is often neglected is compliance by household owners to protect household space after IRS implementation to ensure high effectiveness [10]. One of the operational factors to consider is the timing interval for IRS when multiple rounds are sprayed. A study in Zimbabwe showed that bendiocarb remains active for up to 8 weeks with 96% mortality for mosquitoes on thatch [11]. The same study showed that bendiocarb remains active for up to 20 weeks with 74% mortality for mosquitoes on mud compared to 100% for mosquitoes on thatch [11]. In Madagascar, bendiocarb was shown to have a mortality inducing effect on local mosquitoes of up to 80% for up to 5 months post spraying [12]. Elsewhere in Zanzibar, a study to investigate the residual effect of pirimiphos methyl active ingredient sprayed on common surfaces of human dwellings showed that its mortality inducing effect on local mosquitoes was maintained on all sprayed surfaces up to 8 months post-IRS [13].

The World Health Organization (WHO) recommends scheduling IRS application to coincide with the build-up of vector populations just before the onset of the peak transmission season [14]. WHO further states that it is usually not operationally feasible to conduct more than two rounds of IRS in 1 year [14]. Although these guidelines highlight the importance of the timing of IRS, they do not provide the full information on the optimal interval for insecticides with different protective durations and assume a “one size fits all”. In addition, the information on the effect of factors like seasonality and transmission intensity on the timing interval remain unclear.

In this paper, we provide estimates of optimal timing intervals for IRS with bendiocarb and pirimiphos methyl using malaria incidence data from a cohort of children aged 0.5–10 years living in a historically high malaria transmission area in Uganda. The use of incidence data allows us to test the durability of the spray campaigns on the clinical outcome. We apply a generalized additive model (GAM) to estimate the time-dependent malaria incidence while accounting for observed heterogeneity and later use this incidence to estimate the IRS timing interval. These time interval estimates could be useful to guide policy makers in similar settings on when the next round of IRS should be applied in order to sustain the effect of IRS towards elimination of malaria.

Materials and methods

Study setting

We used clinical data collected between October 2014 and March 2019 from a cohort of participants under the Program for Resistance, Immunology, Surveillance and Modelling of malaria (PRISM) project. The PRISM study was conducted in Nagongera sub-county Tororo district in Uganda, a traditionally high malaria transmission setting whose EIR was estimated at 310 infectious bites per person year in 2010 before the introduction of IRS [15]. Malaria transmission in Uganda is mostly perennial, so even though there are slight seasonal peaks, transmission continues all year making the durability of the IRS product an important consideration. IRS was introduced in Tororo for the first time in December 2014, and six rounds of IRS were applied during the study period. Three rounds of IRS with bendiocarb were applied approximately every six months (December 2014 –January 2015, June–July 2015 and November–December 2015), and three additional rounds of IRS with pirimiphos methyl were applied annually (June-July 2016, June-July 2017 and June-July 2018). The length of time in months it took to complete delivery of each spray campaign were 1.9 for round 1, 1.3 for round 2, 0.8 for round 3, 1.0 for round 4, 2.5 for round 5, and 1.3 for round 6. In May 2017, there was universal distribution of long-lasting insecticidal nets (LLINs) in Tororo as part of the National distribution campaign. Studies have shown an additional effect of LLINs over IRS [16]. The effect of LLINs on the IRS timing interval has been assessed in this analysis.

Study population

The PRISM project had two phases. Phase I of the project has previously been described [15]. Briefly, the study ran between August 2011 and September 2017. In this phase, participants were recruited from 100 randomly selected households within the catchment area of Nagongera Health Centre IV. All children aged 6 months to 10 years and a primary adult caretaker in the selected household were enrolled. Participants were treated for all their health care needs and followed every 1–3 months to measure microscopic and sub-microscopic parasitemia at a dedicated clinic that was open 7 days a week. Clinical malaria was defined as having fever and a positive blood smear. Participants were treated at the study clinic for any febrile illness and symptomatic malaria was documented using passive surveillance. The cohort was dynamic and all children in the participating households that reached 6 months of age were enrolled and children greater than 10 years of age were excluded from further follow-up.

The second phase of PRISM was carried out between October 2017 and March 2019, four years after the role out of IRS. In this phase, 80 households including 33 households from PRISM phase 1 and 47 new randomly selected households. Unlike Phase 1, all household members of the selected households were enrolled into the cohort and followed at the dedicated clinic every four weeks. Follow-up procedure in the Phase 2 were similar to that of Phase 1. All participants in both phases of the study received a LLIN at enrolment. In order to work with the same population from the two phases of PRISM, the analysis in this study was restricted to only children aged 0.5–10 years.

Generalized additive model (GAM)

We used a GAM model to allow a non-parametric dependency of malaria incidence on time in weeks measured from each round of IRS and a parametric component to adjust for other covariates including age group, monthly rainfall (to adjust for seasonality), transmission intensity (using monthly entomological inoculate rate, EIR), and the duration (in months) since mosquito nets were distributed. EIR was calculated as a product of the daily human biting rate (HBR) and the sporozoite rate × 365 days/year as previously described by our group [17]. The GAM model was preferred over parametric models like generalised linear model (GLM) due to the non-linear and complex relationship between time and malaria incidence as shown in Fig 1. The outcome of interest was malaria incidence per person-years calculated for each week from the date of IRS application for each round. The incidence was obtained by dividing the number of malaria cases per week by total time at risk (person time) and was later converted into annual rates. The GAM model was formulated as follows [18].

Fig 1

Incidence of malaria per person-years among children between October 2014 (just before the first IRS round) and March 2019 following 6 rounds of IRS at a site of medium transmission in Uganda.

The vertical lines indicate time points at which the rounds of bendiocarb (dotted-black lines) and pirimiphos methyl (dashed-red lines) were applied, by age group. The size of the points corresponds to the number of children at risk.

Let Y be the outcome, which is malaria incidence per person-years at the j week for the k round of IRS; t be the j week after the k round of IRS modelled non-parametrically; x be a p × 1 vector of p-covariates for the parametric component. A GAM model then relates the mean E(Y) = μ to the time t and covariates x, through a link function as follows [18]; where g(μ) is a monotonic differentiable link function (e.g., identity in our case); β is a vector of parameters associated with the parametric model components for the k round of IRS; f(t) is a centred twice differentiable smooth function for k = 1,2,…,r rounds of IRS.

Many techniques have so far been developed to estimate the smooth effects of the continuous variables in f(t). In this paper, spline smoothing (also referred to as spline regression) is applied. For details about spline smoothing and GAM modelling, we refer the reader to Wahba [19], Reinsch [20], and Brumback & Rice [21].

For the IRS timing interval, we aim to obtain the value of time t for which we observe the first increase in the malaria incidence. Here forth, we refer to the time at which we observe the first increase in malaria incidence after each round of IRS as optimal time or optimal point.

During model building, we used a generalised cross-validation (GCV) score and deviance to check for model fit. A GAM with a smaller GCV score and higher explained deviance implied a better fit. For rainfall, we performed a sensitivity analysis considering 1-month, 2-months and 3-months lags.

Ethics statement

All children aged 0.5–10 years who fulfilled the selection criteria and had written informed consent from a parent or guardian from each household were enrolled into the cohorts in Phase 1 and 2. Both phases of the PRISM study were approved by the Uganda National Council for Science and Technology (HS 1019 for Phase 1 and HS-119ES for Phase 2), Makerere University School of Medicine Research and Ethics Committee (2011–167 for Phase 1 and 2017–099 for Phase 2), the University of California, San Francisco Committee on Human Research (11–05995 for Phase 1 and 17–22544 for Phase 2).

Results

In total, 494 children aged 0.5–10 years contributed to the clinical data observed between October 2014 and March 2019, with a majority (67.0%) less than 5 years of age at enrolment. Table 1 shows number of children at risk and malaria incidence per person-year for 10 six-months periods (2 baseline and 8 post IRS) and 1 five-months period (after the last round of IRS), presented overall and by age group. Malaria incidence decreased from 3.07 per person-year during the first baseline period (November 2013-April 2014) to 2.96 during the second baseline period (May-October 2014) and then to 1.74 during the first period following the first round of IRS application (November 2014-April 2015). The incidence declined to 0.02 per person-year during the last period following the 6th round of IRS. The incidence was higher among children aged less than 5 years during the two baseline periods and the period following the first round of IRS, after which it was consistently higher among those aged 5–10 years.

Table 1

Number at risk and six-months period malaria incidence comparing baseline and post IRS periods.

	Overall	<5 years¶	5–10 years¶
November 2013 –April 2014b	n = 290	n = 125	n = 180
Person years	137.59	54.64	82.95
Incidence per person-year	3.07	3.95	2.50
May 2014 –October 2014 b	n = 289	n = 118	n = 189
Person years	139.95	54.94	85.01
Incidence per person-year	2.96	3.40	2.67
November 2014 –April 2015	n = 282	n = 108	n = 190
Person years	136.81	48.0	88.81
Incidence per person-year	1.74	1.94	1.63
May 2015 –October 2015	n = 288	n = 104	n = 189
Person years	136.86	47.39	89.48
Incidence per person-year	0.83	0.80	0.85
November 2015 –April 2016	n = 279	n = 103	n = 188
Person years	136.35	48.17	88.18
Incidence per person-year	0.095	0.08	0.10
May 2016 –October 2016	n = 275	n = 94	n = 194
Person years	130.70	43.05	87.66
Incidence per person-year	0.46	0.39	0.49
November 2016 –April 2017	n = 210	n = 68	n = 152
Person years	4.17	1.35	2.82
Incidence per person-year	4.56	0.00	6.73
May 2017 –October 2018	n = 365	n = 170	n = 203
Person years	12.61	5.49	7.12
Incidence per person-year	5.87	3.10	8.00
November 2018 –April 2018	n = 266	n = 150	n = 131
Person years	126.2	67.19	59.01
Incidence per person-year	0.03	0.03	0.03
May 2018 –October 2018	n = 267	n = 139	n = 145
Person years	129.41	64.15	65.26
Incidence per person-year	0.10	0.08	0.12
November 2018 –March 2019	n = 271	n = 131	n = 152
Person years	129.41	48.82	57.58
Incidence per person-year	0.02	0.00	0.03

b Baseline period

cohort age used for the age groups, n = number at risk.

Fig 1 shows monthly incidence of malaria for the period running from October 2014 to March 2019 indicating different points of application of the two types of IRS insecticides (bendiocarb and pirimiphos methyl). We observe down and upward trends in the incidence after each round of IRS with no clear difference between children aged less than 5 and those aged 5–10 years. We also observe transmission peaks in the months of May and June which coincided with the second, fourth, fifth and the sixth rounds of IRS.

Though the 2-month lag of rainfall fitted the data better than 1-month and 3-month lags, it did not improve the overall fit (p = 0.105) and it was dropped. The other variables that did not improve model fit and were dropped from the model included age group (p = 0.270), monthly EIR (p = 0.218) and the duration in months since nets were distributed (p = 0.249). The final fitted GAM included only the intercept for the parametric component and the splines for each round of IRS for the non-parametric component with explained deviance of 80.8% and a minimized GCV score of 0.133.

Table 2 shows the parameter estimates, standard errors and corresponding test results of the fitted GAM. The estimated effective degrees of freedom for the smooth terms differed by IRS round and were each different from 1, implying that a linear relationship was not feasible in our case. A diagnostic plot showing residuals against time with the estimated smooth parameters is given in Fig 2. The horizontal lines around 0 indicate better fits for the smoothers for all rounds of IRS except for round 1 and 2 of bendiocarb, which is due to a smaller number of children that were at risk in the last weeks.

Table 2

Estimates of the fitted GAM model using B-splines.

Effect		Estimate (SE)	t-value	p
Intercept		0.30 (0.03)	9.42	<0.001
Smooth effects		Effective degrees of freedom	F-value	p
Time since IRS round (weeks):
Bendiocarb	Round 1	7.00	77.75	<0.001
	Round 2	4.59	16.17	<0.001
	Round 3	1.79	3.88	0.019
Pirimiphos methyl	Round 4	6.99	12.16	<0.001
	Round 5	4.41	14.99	<0.001
	Round 6	4.82	2.35	0.037

Fig 2

Diagnostic plots showing residuals for the fitted B-splines with 95% credible regions (dotted lines) verses time (in weeks) after each round of bendiocarb (top row) and pirimiphos methyl (bottom row) insecticides using different degrees of freedom (in brackets on the y-axis). A straight horizontal line through point 0 on the y-axis implies better fit.

Fig 3 shows observed and predicted weekly malaria incidence for each round of the two types of insecticides (bendiocarb and pirimiphos methyl). The figure also shows the amount of rainfall in mm recorded at different calendar months (vertical bars). The vertical dotted lines in Fig 3 indicate time points at which we first observe an increase in malaria incidence following a decreasing trend after each round of IRS. These points refer to optimal time for applying another round of IRS. Results suggest that the optimal time for applying another round of bendiocarb after the first round was 17 weeks (95% confidence interval (CI): 14.2–21.0) (about 4 months) and 40 weeks (95% CI: 37.0–42.8) (about 10 months) after the first round of pirimiphos methyl. The optimal time for applying another round of pirimiphos methyl after the third round (round 6 overall) was 36 weeks (95% CI: 32.7–37.7) (about 9 months). After the second rounds of bendiocarb and pirimiphos methyl (round 5 overall), the incidence continuously decreased and the minimum point could not be established. After the third round of bendiocarb, unlike the other rounds of IRS, the incidence continuously increased and we were not able to estimate the optimal time. The figure also indicates that malaria incidence after each round of IRS does not depend on the amount of rainfall and consequently the optimal time for IRS.

Fig 3

Observed (blue points) and predicted (solid red line) incidence of malaria per person-years against time in weeks since application of IRS and monthly rainfall (vertical orange bars) against calendar time for each round of bendiocarb (top row) and pirimiphos methyl (bottom row). The secondary y-axis labelled in orange represents the amount of monthly rainfall in mm. The vertical dotted lines indicate optimal time for applying another round of IRS.

Discussion

Our study suggests that for effective malaria control, the optimal time for applying another round of IRS after the first one is 17 weeks for bendiocarb and 40 weeks for pirimiphos methyl. After the third round of pirimiphos methyl, a slightly shorter interval of 36 weeks was found optimal before another round. We could not establish from the data the optimal time for applying another round of IRS after the second and the third rounds of bendiocarb as well as after the second round of pirimiphos methyl due to failure to attain the minimum points. We used a generalized additive model (GAM) to estimate malaria incidence, where time in weeks from each round of IRS was modelled non-parametrically and later used the time-dependent incidence to obtain optimal time for IRS application. We applied the model to data from a cohort study of children aged 0.5–10 years from an area of previously high transmission (Tororo district) in Uganda [15].

Though our findings agree that pirimiphos methyl is longer-acting than bendiocarb [22, 23], the results suggest shorter intervals than those claimed by the manufactures of these insecticides [23] and as applied during the study implementation [24]. For example, instead of considering 6 months (24 weeks) after each round of bendiocarb as was applied during the study, a shorter interval of about 4 months after the first round could be appropriate. Also, instead of the 12 months interval after each round of pirimiphos methyl which was the case during the study, the findings suggest about 10 months for the second and 9 months for the fourth round. These findings also suggest that for bendiocarb, more than 2 rounds of IRS in a year could be required which contradicts with WHO recommendation of not more than two rounds per year [14]. However, for pirimiphos methyl, the results suggest that about one round per year is feasible and this concurs with the WHO recommendations [14]. The timing intervals suggested by our results also concurs with results by the study in the Northern part of Uganda (period 2012–2015) where incidence in the seventh month following IRS was almost twice that of the last spray month with IRR of 1.75 [25]. However, the authors did not specify the type of insecticide used. This implies that waiting for 7 months in case of bendiocarb before another round of IRS may leave communities unprotected.

The finding on increase in malaria incidence after IRS application is similar to what other authors reported. For example, other studies in Uganda documented increases in malaria incidence [25] and test positivity rate (TPR) post IRS [24, 26]. This could be due to poor timing of IRS relative to the transmission peak. For example, our results indicate higher transmission peak in May/June time, so perhaps the November spray rounds may have artificially bigger impacts due to the natural decline in transmission observed during these months of the year.

The findings show that the timing interval was not altered by the amount of rainfall, the entomological inoculation rate (EIR), and the duration in months from when nets were distributed. For rainfall and EIR, perhaps it is because the data we used were already aggregated by month. Finer intervals like weekly data would help to improve our estimates but were not available.

This work highlights well the variability in the length of time that the active ingredient deployed during a spray campaign may have, even for the same product within a single setting. Real-time surveillance of trends in clinical incidence could be used to help advise strategy teams on when to re-deploy the IRS intervention, ensuring rotation of products as guided by insecticide resistance management policies [14].

The findings in this study have four limitations. First, the results presented here were limited to one site, hence conclusions may not be generalized. Secondly, the study was not designed to estimate the timing interval for IRS. A future study designed to estimate the timing interval for IRS could help improve our estimates. Thirdly, our analysis did not consider asymptomatic infections which could possibly alter our estimates. For example, in the study setting the prevalence of asymptomatic infection was estimated at 58% in October 2014 (baseline) and 7% in February 2019 (end line) [17]. Fourth, we assume no change in the level of insecticide resistance to either product throughout the time-series. We did not have susceptibility bioassays for the study region to corroborate this assumption.

In conclusion, for effective control of malaria using IRS, different spraying intervals are needed for different rounds of bendiocarb and pirimiphos methyl insecticides. For example, the second round of bendiocarb needs to be applied 17 weeks (about 4 months) from the first round, and for pirimiphos methyl, 40 weeks (10 months) for round two, and 36 weeks (9 months) for round four. These intervals were not altered by the amount of rainfall, EIR, and the duration in months since nets were distributed. Though shorter intervals for IRS than the usual practice are suggested by this study, cost-benefit analysis should be practiced to balance between effectiveness of IRS and implementation costs and insecticide resistance.

(CSV)

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7 Apr 2020

PONE-D-20-03773

Estimating the Timing Interval for Malarial Indoor Residual Spraying: A Modelling Approach

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Reviewer #1: General comments

This article is timely and addresses a very important neglected component of IRS that can effectively improve its impact, and it is definitely worth publishing and communicating the information to the implementing programs. Even though the author has addressed most of the relevant points, the following aspects, comments and questions need clarification before considered for publishing this article.

Introduction:

• The authors have explained that IRS has been shown to be effective in terms of reducing malaria incidences and entomological indicators in various settings. However, in several other studies conducted from other endemic settings across Africa, contrasting results have also been documented, and this does not devalue the importance of IRS. So I suggest to the authors to include the two contrasts in their paper and perhaps, as timing could partly play a role in the negative outcomes reported in other endemic settings alongside other factors.

• The authors have indicated factors that in general may affect the overall effectiveness of IRS such as individual insecticides, operational factors etc., however, one of the key indicator or factor crucial to the effectiveness of IRS is compliance even though extremely neglected for IRS, which is basically protecting the personal household space after IRS implementation to ensure high effectiveness of the product. I also suggest here to the authors to include human behavioural factor as various activities conducted by household owners within personal household spaces may greatly affect the IRS efficacy and hence needs to be considered as a key component while planning for IRS implementation periods.

Methodology:

• LLINs were distributed almost less than 2 months before IRS implementation, in 2017, and given that nets (pyrethroid nets) have been shown to repel or irritate mosquitoes, is there any masking effect that may have led to the assumption that incidence greatly reduced as a result of IRS?

Reviewer #2: This is an interesting analysis of clinical incidence data tracking a cohort of under 10-year olds living in a high transmission setting that has implemented IRS. During Phase I, the study explores the use of bendiocarb sprayed approximately bi-annually. During phase II, the study explores annual implementation with Actellic 300®CS. The analysis is robust, and this is an interesting method to determine the window between spray rounds. However, I have some major concerns for the authors to consider prior to acceptance for publication.

Major concerns

1. The authors do not mention the natural seasonality of mosquitoes, how their density may change in the absence of chemical intervention, or whether these changes subsequently drive any seasonality in clinical incidence. Please could the authors comment on how this was accounted for in the statistical model as an increase in cases (the optimal point) could still indicate a working IRS product if the cases increase at a slower rate than expected in the absence of said product at specific times of the year. Were there any data on mosquito vector densities to corroborate the impact on incidence is driven by the IRS impact?

2. How was clinical incidence monitored in the trial? Were rapid diagnostic tests used to confirm positivity? This is important because if the presence of fevers are used to indicate malaria positivity (the authors note symptoms, but do not specify what these are for this study) then ‘true’ seasonality may be dampened because of other infections causing fever that are mis-diagnosed to be malaria. Please comment to clarify.

3. As all participants also received an ITN on enrolment, how were the impacts of these interventions decoupled from the impact of the IRS products? How were the effects adjusted for later rounds of IRS when the ITN efficacy would have waned and adherence to ITN use may have diminished?

In addition, were ITNs redistributed at any point through the study? If not, is the impact from Actellic even greater because it acts with older ITNs – potentially used less by the community and almost certainly working less effectively because the active ingredient has waned. It is critical to comment or account for this effect.

4. Given that malaria cases are so sensitive to the local ecology for any given transmission season, could the authors comment on the uncertainty in their suggested interval for spray rounds and whether they can infer anything from the statistical model about how this window may vary season to season, year to year or across any other boundary. Is it possible to make any sort of prediction for other settings using this framework? The authors note that uncertainty will be considered in future work but this should be included here. Could you infer uncertainty from the 90% credible intervals of the Bayesian simulations carried through from the splines? Or use the mean +/- 1.96*SE to estimate some uncertainty for the spline predictions shown in Fig 3?

Is there any sense in fitting the estimate across rounds for each chemistry? Rather than after each round separately to try and infer a more generalised estimate for this interval between spraying? Could the authors source data from other sites to refit the model with additional data which would enable the uncertainty to be better captured and results to be generalised which would be more useful for guiding policy decisions.

Please see additional comments in the attached document.

Reviewer #3: Manuscript: Estimating the Timing Interval for Malarial Indoor Residual Spraying: A Modelling Approach

The authors provided some interesting data for effective use IRS in malaria control, thought there are issues that need attention

One is the cost effectiveness of the repeated application of the IRS, which is also directly related with the sustainability of the program

Another one is that the authors indicated the malaria incidence at baseline month, midline month and endline month, but not in between. Fig 1 shows some interesting results—where more malaria cases are observed immediately after IRS of both insecticides.

What would happen if the first application miss correct timing—which month/or season is suitable for the first IRS? The first IRS timing not affect the results?

What would happen if the actellic insecticide is the only insecticide applied throughout the study period? --- 40 weeks intervals still works? What about the bediocarb alone?

The authors didn’t indicate the impact of bed nets independently? Bed nets could affect the interval?

Fig 2 and 3---hard to understand— possible to simplify

Methods: Was it systematically designed to answer the question? For example – the number of children in phase 1 and 2 are different? This is weakest part of this study, and lead to the results of inconclusive.

Discussion: ….The finding on increase in malaria incidence after IRS application is similar to what other authors reported. For example, other studies in Uganda documented increases in malaria incidence and test positivity rate (TPR) post IRS…. not justifiable results

This result need another justification ….method section and the initial spray time

Conclusions: there are a lot of uncertainty to reach these conclusions

Finally, major revision is required if considered for publication

**********

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Reviewer #3: Yes: Fekadu Massebo (Dr)

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15 May 2020

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Response: The funding-related text is now removed from the manuscript. Please revise the funding statement to read as follows: “This research report is supported by the National Institute of Allergy and Infectious Diseases (NIAID) as part of the International Centers of Excellence in Malaria Research (ICEMR) program (U19AI089674) and the Fogarty International Center of the National Institutes of Health under Award Number D43TW010526. JIN is supported by the Fogarty International Center (Emerging Global Leader Award grant number K43TW010365)”

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Response: Done. The ethics statement has been transferred under methods section and deleted where it was placed just before reference. Lines 157-164 of manuscript.

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1 Jul 2020

PONE-D-20-03773R1

Estimating the Optimal Interval between Rounds of Indoor Residual Spraying of Insecticide Using Malaria Incidence Data from Cohort Studies

PLOS ONE

Dear Dr. Mugenyi,

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Additional Editor Comments (if provided):

Dear Author, please see the following comments from Reviewer 2. They submitted these in a word document.

Review

Article: PONE-D-20-03773R1

Title: Estimating the Optimal Interval between Rounds of Indoor Residual Spraying of Insecticide Using Malaria Incidence Data from Cohort Studies

Summary: The work tracks clinical incidence data for a cohort of 6-month to 10-year olds living in a high transmission setting that has implemented IRS. During Phase I, the study explores the use of bendiocarb sprayed approximately bi-annually. During phase II, the study explores annual implementation with Actellic 300®CS. The limitations of the study are better explained in this revision. However, I still have some major reservations.

There remain a few queries on the methods and I have some hesitations on how the results are discussed – particularly in the abstract

Thank you for providing the data, that is useful for the review process. How was the annual EIR calculated? Please add a few sentences to explain how this was done with the data available. Similarly, it looks like rainfall was estimated monthly or over longer periods – is it possible to get finer scale information on rainfall – by week perhaps? This could impact dramatically on whether it is statistically important.

The methods first focus on the statistical methods, but it may be more intuitive to present the study area, data collection and PRISM trial prior to the statistics to help the reader follow the work reported.

Have you tried to include a variable like ‘month since net distribution’ or even a binary ‘net distribution within 1 year’ variable to try to account for the distinct impact seen from new nets?

The authors present, in the abstract, the clinical incidence from October 2014 and February 2019. But this could be driven by seasonality. It would be better to compare two times from the same month of the year, or even better still, averaging clinical incidence across a wider window of time to mitigate somewhat for natural between-year stochasticity in the metric. For how many months were the children tracked prior to application of the spray? Could you average across this entire period and compare it to the same length of time (and months) in the post application periods? Can you also provide the baseline data in the supplement?

I have the same issue with Table 1.

Table 1 is not so useful as it currently stands – it could be more informative to average or sum clinical incidence in the tracked cohorts across longer time windows (matching months) as, although seasonal rainfall did not appear to be associated with the duration of IRS effectivity in the GAM analysis, seasonal patterns in malaria incidence are likely and do tend to drive transmission by increasing the EIR, so comparing different times of the year may be misleading. Again, averaging or incidence over 3 or 6 months at baseline and comparing that to the same months in later years would be a fairer presentation. This seems particularly important because Fig 1 seems to suggest a higher mean incidence in Oct 2016 (than the baseline Oct 2014 estimate) but low in Dec 2016 – which is chosen as the comparison estimate … this is not a fair comparison so estimating the average clinical incidence across multiple months to some extent could overcome this potential stochasticity in measurements and alleviate the need to arbitrarily select a single measurement for the table.

I have also a suggestion for the abstract as I think that the methods/results and particularly conclusions paragraphs could be more useful if altered slightly.

• Use the IRS product (presumably Ficam® and Actellic 300®CS) or the insecticide active ingredient (bendiocarb and pirimiphos methyl) not one of each [this issue applies throughout the manuscript]

• Present the empirical data first, then the statistics

• Refer to the model as a statistical approach (as it is not a predictive model as the mechanism of transmission is not determined), so statistical approach is clearer.

• My greatest hesitation is with the conclusion. Here, a statistical model is applied which is useful to capture the direction of an effect or compare the two IRS products, but it is not useful for future projections because it does not capture the mechanism of impact. So, inferring from the statistical model needs to be done with care. Drawing contrasts between the spray applications is reasonable, but perhaps soften how this is stated so as not to determine future application.

Below is a suggestion using tracked changes [comments to address/delete!]:

“Background : Indoor residual spraying (IRS) reduces vector densities and malaria transmission, however, the most effective spraying intervals for IRS have not been well established. We estimated the optimal timing interval for IRS using a statistical approach.

Methods : Six rounds of IRS were implemented in Tororo District, a historically high malaria transmission setting in Uganda, during the study period (3 rounds with Ficam (bendiocarb active ingredient): December 2014 to December 2015, and 3 rounds with Actellic 300®CS (pirimiphos methyl active ingredient): June 2016 to July 2018). Generalized additive models were used to estimate the optimal timing interval for IRS based on the predicted malaria incidence. The model was fitted to clinical incidence data from a cohort of children aged 0.5–10 years from selected households observed throughout the study period. Annual entomological inoculation rates were estimated [how] and aggregated monthly [looking at the data file it looks like these were averaged?] .

Results : Monthly incidence of malaria from October 2014 to February 2019 decreased from 3.25 to 0.0 per person-years in the children under 5 years, and 1.57 to 0.0 for 5-10 year-olds [this is not appropriate, please see comments above]. The optimal time interval for IRS differed between bendiocarb and pirimiphos methyl and by IRS round. To retain an optimum impact, Ficam® would require respraying 17 weeks after application whereas Actellic 300®CS could remain impactful for 40 weeks, although in the final year this estimate 36 weeks. However, we could not estimate from the data the optimal time after the second and third rounds of bendiocarb and after the second round of actellic. Neither the amount of rainfall nor the EIR were found to be statistically significant for determining the time period between spray rounds [although these are aggregated by month(?) so the data may lack the scale needed to identify impact].

Conclusion: In our setting, the effect of the two IRS products was distinct. Statistically, Actellic 300®CS provided a longer window of protection than Ficam®, although impact varied between different spray rounds and years which was not explained by rainfall or EIR in our statistical approach. Understanding the effectiveness of IRS and how long it lasts can help for planning campaigns, but one should consider the financial cost and insecticide resistance. Monitoring the timing of spray campaigns using clinical incidence could be repeated in future programs to help determine the average period of protectivity of these products.”

There is no discussion on the timing of the intervention relative to the transmission peak. Generally, Uganda has a higher transmission peak in May/June time, so perhaps the November spray rounds have artificially bigger impacts due to fewer cases / less transmission in these periods of the year.

Thank you for adding the sentence on fever and blood smear positivity. Just to clarify, did the blood smear have to be positive for a clinical case to be scored? Or were some patients classed to be malaria positive by fever only? This could alter assumptions.

It may be worth including a sentence or two on what it means for the analysis to miss asymptomatic cases and what proportion of the population may be asymptomatic (a good paper on this issue e.g. Lindsay Wu et al Nature 2015: https://www.nature.com/articles/nature16039?proof=true1)

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed most of the comments, however, for clarity of the information the minor comments below needs to be addressed before publishing.

Additional Minor concerns:

I understand the author’s focus is on timing of IRS with benthiocarb and Actellic, however, I still think there is a missing link on how the author has addressed the question 1 linked to paragraph one in the introduction section. In my opinion not only non-pyrethroids have produced contrasting impacts.

In the methodology section, the author has addressed that studies have shown that LLIN may have effect yet only referenced 1 article. I also think the statement requires a bit of modification to suit its placement in the methodology section, otherwise the author has mentioned a similar comment in the discussion section, under limitation so either way do away with it in methodology section or rephrase.

Reviewer #2: Please see the attached response to authors

I have indicated that not all data are provided but only because the baseline data are not shown and these would be useful. Thank you for providing the remaining data

I have some concerns on using the monthly (or longer) estimates of rainfall and EIR for associations with timing of spraying. This may not be a fine enough resolution to usefully determine an association and may explain why none were found.

I have some concerns on the interpretation and conclusions as a consequence. These are addressed in the attached review.

**********

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Reviewer #1: No

Reviewer #2: No

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25 Aug 2020

To: The Editor-in-Chief

PLOS ONE

Reference number: PONE-D-20-03773

Thank you very much for the thoughtful reviews of our manuscript and for inviting us to re-submit our revised manuscript (based on the reviewers’ comments) titled: “Estimating the Optimal Interval between Rounds of Indoor Residual Spraying of Insecticide Using Malaria Incidence Data from Cohort Studies”. Below are point by point responses to the reviewers’ comments. We confirm that the material in this manuscript has not and will not be offered elsewhere for possible publication, as long as it is under PLOS ONE consideration. We look forward to your favourable consideration of this re-submission.

POINT BY POINT RESPONSES TO THE REVIEWERS’ COMMENTS.

Reviewer 1:

Review

Article: PONE-D-20-03773R1

Title: Estimating the Optimal Interval between Rounds of Indoor Residual Spraying of Insecticide Using Malaria Incidence Data from Cohort Studies

Summary: The work tracks clinical incidence data for a cohort of 6-month to 10-year olds living in a high transmission setting that has implemented IRS. During Phase I, the study explores the use of bendiocarb sprayed approximately bi-annually. During phase II, the study explores annual implementation with Actellic 300®CS. The limitations of the study are better explained in this revision. However, I still have some major reservations.

There remain a few queries on the methods and I have some hesitations on how the results are discussed – particularly in the abstract

Thank you for providing the data, that is useful for the review process. How was the annual EIR calculated? Please add a few sentences to explain how this was done with the data available. Similarly, it looks like rainfall was estimated monthly or over longer periods – is it possible to get finer scale information on rainfall – by week perhaps? This could impact dramatically on whether it is statistically important.

Response:

Annual EIR defined as the number of infectious bites per person per year was calculated as a product of the daily human biting rate (HBR) and the sporozoite rate × 365 days/year. The following sentence has been added in the methodology “EIR was calculated as a product of the daily human biting rate (HBR) and the sporozoite rate × 365 days/year as previously described by our group (16)”. See lines 135-136 of the manuscript.

Unfortunately, rainfall data was received when already aggregated by month and therefore we could not revise the results using weekly data. A discussion of this has been added as follows “For rainfall and EIR, perhaps it is because the data we used were already aggregated by month. Finer intervals like weekly data would help to improve our estimates.” See lines 272-274 of the manuscript.

The methods first focus on the statistical methods, but it may be more intuitive to present the study area, data collection and PRISM trial prior to the statistics to help the reader follow the work reported.

Response: This has been revised as suggested. See lines 96-161 of the manuscript.

Have you tried to include a variable like ‘month since net distribution’ or even a binary ‘net distribution within 1 year’ variable to try to account for the distinct impact seen from new nets?

Response: A variable for months since net distribution has been included in the analysis. However, this did not improve the model fit (p=0.249). Note that nets were distributed at enrolment and there was a universal distribution in May 2017. This result has been presented. See lines 200-201 of the manuscript.

The authors present, in the abstract, the clinical incidence from October 2014 and February 2019. But this could be driven by seasonality. It would be better to compare two times from the same month of the year, or even better still, averaging clinical incidence across a wider window of time to mitigate somewhat for natural between-year stochasticity in the metric. For how many months were the children tracked prior to application of the spray? Could you average across this entire period and compare it to the same length of time (and months) in the post application periods? Can you also provide the baseline data in the supplement?

I have the same issue with Table 1.

Table 1 is not so useful as it currently stands – it could be more informative to average or sum clinical incidence in the tracked cohorts across longer time windows (matching months) as, although seasonal rainfall did not appear to be associated with the duration of IRS effectivity in the GAM analysis, seasonal patterns in malaria incidence are likely and do tend to drive transmission by increasing the EIR, so comparing different times of the year may be misleading. Again, averaging or incidence over 3 or 6 months at baseline and comparing that to the same months in later years would be a fairer presentation. This seems particularly important because Fig 1 seems to suggest a higher mean incidence in Oct 2016 (than the baseline Oct 2014 estimate) but low in Dec 2016 – which is chosen as the comparison estimate … this is not a fair comparison so estimating the average clinical incidence across multiple months to some extent could overcome this potential stochasticity in measurements and alleviate the need to arbitrarily select a single measurement for the table.

Response: Thank you so much for this observation and for the suggestions. We have revised the presentation of our results in Table 1 by looking at the 6-months period before IRS (baseline) compared to the 6-months periods post IRS application. The periods we considered are Nov2013-Apr2014, May2014-Oct2014, Nov2014-Apr2015, May2015-Oct2015, Nov2015-Apr2016, May2016-Oct2016, Nov2016-Apr2017, May2017-Oct2017, Nov2017-Apr2018 and May2018-Oct2018. The incidence declined from 2.96 per person years during the baseline period (May-October 2014) to 1.74 during the first period following the first round of IRS application (November 2014-April 2015) and then to 0.02 during the last period following the 6th round of IRS. See lines 177-186 of the manuscript.

I have also a suggestion for the abstract as I think that the methods/results and particularly conclusions paragraphs could be more useful if altered slightly.

• Use the IRS product (presumably Ficam® and Actellic 300®CS) or the insecticide active ingredient (bendiocarb and pirimiphos methyl) not one of each [this issue applies throughout the manuscript]

Response: Thank you for this observation. Consistency has been obtained throughout the manuscript by using insecticide active ingredient names.

• Present the empirical data first, then the statistics

Response: Empirical data is now presented before statistics. See line 37 of the manuscript.

• Refer to the model as a statistical approach (as it is not a predictive model as the mechanism of transmission is not determined), so statistical approach is clearer.

Response: This has been revised as suggested. See lines 29 and 50 of the manuscript.

• My greatest hesitation is with the conclusion. Here, a statistical model is applied which is useful to capture the direction of an effect or compare the two IRS products, but it is not useful for future projections because it does not capture the mechanism of impact. So, inferring from the statistical model needs to be done with care. Drawing contrasts between the spray applications is reasonable, but perhaps soften how this is stated so as not to determine future application.

Below is a suggestion using tracked changes [comments to address/delete!]:

“Background : Indoor residual spraying (IRS) reduces vector densities and malaria transmission, however, the most effective spraying intervals for IRS have not been well established. We estimated the optimal timing interval for IRS using a statistical approach.

Methods : Six rounds of IRS were implemented in Tororo District, a historically high malaria transmission setting in Uganda, during the study period (3 rounds with Ficam (bendiocarb active ingredient): December 2014 to December 2015, and 3 rounds with Actellic 300®CS (pirimiphos methyl active ingredient): June 2016 to July 2018). Generalized additive models were used to estimate the optimal timing interval for IRS based on the predicted malaria incidence. The model was fitted to clinical incidence data from a cohort of children aged 0.5–10 years from selected households observed throughout the study period. Annual entomological inoculation rates were estimated [how] and aggregated monthly [looking at the data file it looks like these were averaged?] .

Results : Monthly incidence of malaria from October 2014 to February 2019 decreased from 3.25 to 0.0 per person-years in the children under 5 years, and 1.57 to 0.0 for 5-10 year-olds [this is not appropriate, please see comments above]. The optimal time interval for IRS differed between bendiocarb and pirimiphos methyl and by IRS round. To retain an optimum impact, Ficam® would require respraying 17 weeks after application whereas Actellic 300®CS could remain impactful for 40 weeks, although in the final year this estimate 36 weeks. However, we could not estimate from the data the optimal time after the second and third rounds of bendiocarb and after the second round of actellic. Neither the amount of rainfall nor the EIR were found to be statistically significant for determining the time period between spray rounds [although these are aggregated by month(?) so the data may lack the scale needed to identify impact].

Conclusion: In our setting, the effect of the two IRS products was distinct. Statistically, Actellic 300®CS provided a longer window of protection than Ficam®, although impact varied between different spray rounds and years which was not explained by rainfall or EIR in our statistical approach. Understanding the effectiveness of IRS and how long it lasts can help for planning campaigns, but one should consider the financial cost and insecticide resistance. Monitoring the timing of spray campaigns using clinical incidence could be repeated in future programs to help determine the average period of protectivity of these products.”

Response: Thank you so much for these suggestions. The abstract has been revised using your suggested edits. However, we preferred using active ingredient names instead of trade names. See lines 27-53 of the manuscript.

There is no discussion on the timing of the intervention relative to the transmission peak. Generally, Uganda has a higher transmission peak in May/June time, so perhaps the November spray rounds have artificially bigger impacts due to fewer cases / less transmission in these periods of the year.

Response: Thank you so much for this observation. The following sentence acknowledging transmission peaks in the months of May-June has been added “We also observe transmission peaks in the months of May and June which coincided with the second, fourth, fifth and the sixth rounds of IRS.” See lines 190-192 of the manuscript.

A discussion on November spray rounds has been added in the discussion as a possible explanation for the increase in the incidence after IRS as follows: “This could be due to poor timing of IRS relative to the transmission peak. For example, our results indicate higher transmission peak in May/June time, so perhaps the November spray rounds may have artificially bigger impacts due to less transmission in these periods of the year”. See lines 267-270 of the manuscript.

Thank you for adding the sentence on fever and blood smear positivity. Just to clarify, did the blood smear have to be positive for a clinical case to be scored? Or were some patients classed to be malaria positive by fever only? This could alter assumptions.

Response: Yes, all malaria cases were first confirmed by a positive blood smear. A case of malaria was defined as having a fever (tympanic temperature > 38.0°C or history of fever in the previous 24 hours) and a positive thick blood smear was by light microscopy.

It may be worth including a sentence or two on what it means for the analysis to miss asymptomatic cases and what proportion of the population may be asymptomatic (a good paper on this issue e.g. Lindsay Wu et al Nature 2015: https://www.nature.com/articles/nature16039?proof=true1)

Response: Although the data on asymptomatic cases is available for this cohort, we used clinical malaria as the outcome as these are the individuals who would routinely be identified at the health facilities and are the main group that would drive policy decisions in an endemic setting. The prevalence of asymptomatic parasitemia was estimated at 58% in October 2014 (baseline) and 7% in February 2019 (end line). Details of the prevalence of parasitemia by age-group has been published elsewhere (Nankabirwa et al. 2020). The limitation section has been updated to capture this as follows. “Thirdly, our analysis did not consider asymptomatic infections which could possibly alter our estimates. For example, in the study setting the prevalence of asymptomatic infection was estimated at 58% in October 2014 (baseline) and 7% in February 2019 (end line)(16)”. See lines 278-281 of the manuscript.

Reviewer 2:

Additional Minor concerns:

I understand the author’s focus is on timing of IRS with benthiocarb and Actellic, however, I still think there is a missing link on how the author has addressed the question 1 linked to paragraph one in the introduction section. In my opinion not only non-pyrethroids have produced contrasting impacts.

Response: Non-pyrethroids has been dropped and the sentence now read as follows: “However, some IRS products have contrasting impacts(8).” See line 64 of the manuscript.

In the methodology section, the author has addressed that studies have shown that LLIN may have effect yet only referenced 1 article. I also think the statement requires a bit of modification to suit its placement in the methodology section, otherwise the author has mentioned a similar comment in the discussion section, under limitation so either way do away with it in methodology section or rephrase.

Response: The manuscript has been revised: 1) to add new references that have assessed the impact of LLINs on malaria burden, 2) a variable looking at months since distribution of LLINs has been included in the model, although this did not improve the model fit (see lines 200-201 of the manuscript), 3). The statement on LLINs under methodology has been revised as follows “Studies have shown an additional effect of LLINs over IRS(15). The effect of LLINs on the IRS timing interval has been assessed in this analysis.” See lines 107-108 of the manuscript.

Reviewer #2: Please see the attached response to authors

I have indicated that not all data are provided but only because the baseline data are not shown and these would be useful. Thank you for providing the remaining data

Response: Baseline data is now analysed looking at 2 six-months periods before IRS (see Table 1). The remaining selected baseline data are shared.

I have some concerns on using the monthly (or longer) estimates of rainfall and EIR for associations with timing of spraying. This may not be a fine enough resolution to usefully determine an association and may explain why none were found.

Response: Thank you for this information, unfortunately the rainfall data available is already aggregated by month. We appreciate that this could be a long period to determine the associations with malaria incidence. This limitation has been acknowledged in the discussion as below: “The findings show that the timing interval was not altered by the amount of rainfall, the entomological inoculation rate (EIR), and the duration in months from when nets were distributed. For rainfall and EIR, perhaps it is because the data we used were already aggregated by month. Finer intervals like weekly data would help to improve our estimates.” See lines 271-275 of the manuscript.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

24 Sep 2020

PONE-D-20-03773R2

Estimating the Optimal Interval between Rounds of Indoor Residual Spraying of Insecticide Using Malaria Incidence Data from Cohort Studies

PLOS ONE

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Additional Editor Comments (if provided):

Please see some very minor revisions suggested from reviewer 2. Once those are addressed your manuscript will be accepted. Nearly there! And well done!

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Reviewer #2: Yes

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Reviewer #2: Thank you for re-submission. Please see the attached comments also copied below

The work tracks clinical incidence data for a cohort of 6-month to 10-year olds living in a high transmission setting that has implemented IRS. During Phase I, the study explores the use of bendiocarb sprayed approximately bi-annually. During phase II, the study explores annual implementation with Actellic 300®CS. The draft is improved on the previous versions and the previous comments are addressed well. I have a few minor comments to add and a couple of suggestions to pass on to the authors.

Introduction

There is a reference for the statement in lines 68-70: (https://malariajournal.biomedcentral.com/articles/10.1186/s12936-020-3102-6)

This sentence, starting line 70, is not quite correct as thatch is mentioned twice – please correct:

“A study in Zimbabwe showed that bendiocarb remains active for up to 8 weeks with 96% mortality for mosquitoes on thatch, and 20 weeks with 74% mortality for mosquitoes on mud compared to 100% for mosquitoes on thatch (10).”

Similarly the following sentence needs to be adjusted slightly for clarity:

“While in Madagascar, bendiocarb was shown to have an efficacy of up tomaintain 80% mosquito mortality for up to 5 months post spraying (11).”

Throughout this second paragraph of the introduction, efficacy is used without defining exactly what is meant. Perhaps state that a mortality inducing effect where more than 80% of susceptible or local mosquitoes are killed in a bioassay test on the sprayed surfaces is considered effective by WHO guidelines [ref]. (In my opinion, if sprays are killing mosquitoes at a less potent level they may well still have robust impact on incidence – but this is not the WHO definition at the moment!) Otherwise you could stick to quantifying this effect by stating that the mortality inducing effect on local mosquitoes remained above 80% for xx weeks as noted by the referenced studies.

One interesting part of your study is that your tested outcome is a clinical one. Testing IRS on mosquito mortality alone forces us to make an assumption about how reduced mosquitoes may lead to reduced clinical incidence. Your approach allows you to test the durability of the spray campaigns on the clinical outcome. I think this is worth highlighting, either as a sentence in the introduction or discussion.

It is also worth noting that malaria transmission in Uganda is mostly perennial, so even though there are slight seasonal peaks, transmission continues all year making the durability of the IRS product an important consideration. This helps understand the analysis too because the 6-month periods can be more consistently compared than would be the case in an area that had more seasonal transmission.

Discussion

Line 259 should be ‘concurs’ not ‘concur’

On line 264 perhaps say ‘may leave communities unprotected’ rather than may render IRS ineffective

Lines 269- Perhaps say ‘due to the natural decline in transmission observed during these months of the year’.

On line 274 I would simply add – “but were not available.”

Perhaps add as a limitation that you assume no change in the level of insecticide resistance to either product throughout the time-series (if you have susceptibility bioassays for the region perhaps you could corroborate this assumption showing or referencing those).

I think there is a policy suggestion from your analysis that you could reasonably add to the discussion –

This work highlights well the variability in the length of time that the active ingredient deployed during a spray campaign may have, even for the same product within a single setting. Real-time surveillance of trends in clinical incidence could be used to help advise strategy teams on when to re-deploy the IRS intervention, ensuring rotation of products as guided by insecticide resistance management policies [you can ref WHO].

The only other comment I have is that spray campaigns sometimes take months to deliver to a community, so perhaps it is worth noting the length of time, in these communities, that it took to complete delivery of each spray campaign and the ultimate percentage of households that were then covered in each spray round. Does this make a difference to the predictions?! Perhaps beyond the scope of the current paper as it is nearly ready to accept, but something to consider in future perhaps.

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Reviewer #1: Yes: Mercy Opiyo

Reviewer #2: No

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Submitted filename: Reviewer Comments PLoS one 2020_R2.docx

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6 Oct 2020

POINT BY POINT RESPONSES TO THE REVIEWER’S COMMENTS.

Review

Article: PONE-D-20-03773R2

Title: Estimating the Optimal Interval between Rounds of Indoor Residual Spraying of Insecticide Using Malaria Incidence Data from Cohort Studies

Summary: The work tracks clinical incidence data for a cohort of 6-month to 10-year olds living in a high transmission setting that has implemented IRS. During Phase I, the study explores the use of bendiocarb sprayed approximately bi-annually. During phase II, the study explores annual implementation with Actellic 300®CS. The draft is improved on the previous versions and the previous comments are addressed well. I have a few minor comments to add and a couple of suggestions to pass on to the authors.

Introduction

There is a reference for the statement in lines 68-70: (https://malariajournal.biomedcentral.com/articles/10.1186/s12936-020-3102-6)

Response: Thank you so much for the suggested reference. This has been added. See lines 68-70

This sentence, starting line 70, is not quite correct as thatch is mentioned twice – please correct:

“A study in Zimbabwe showed that bendiocarb remains active for up to 8 weeks with 96% mortality for mosquitoes on thatch, and 20 weeks with 74% mortality for mosquitoes on mud compared to 100% for mosquitoes on thatch (10).”

Response: Thank you for this observation. The sentence has been revised to read as follows: “A study in Zimbabwe showed that bendiocarb remains active for up to 8 weeks with 96% mortality for mosquitoes on thatch(11). The same study showed that bendiocarb remains active for up to 20 weeks with 74% mortality for mosquitoes on mud compared to 100% for mosquitoes on thatch(11).” See lines 71-74

Similarly the following sentence needs to be adjusted slightly for clarity:

“While in Madagascar, bendiocarb was shown to maintain 80% mosquito mortality for up to 5 months post spraying (11).”

Response: The sentence has been revised to read as follows: “In Madagascar, bendiocarb was shown to have a mortality inducing effect on local mosquitoes of up to 80% for up to 5 months post spraying (12).” See lines 74-76.

Throughout this second paragraph of the introduction, efficacy is used without defining exactly what is meant. Perhaps state that a mortality inducing effect where more than 80% of susceptible or local mosquitoes are killed in a bioassay test on the sprayed surfaces is considered effective by WHO guidelines [ref]. (In my opinion, if sprays are killing mosquitoes at a less potent level they may well still have robust impact on incidence – but this is not the WHO definition at the moment!) Otherwise you could stick to quantifying this effect by stating that the mortality inducing effect on local mosquitoes remained above 80% for xx weeks as noted by the referenced studies.

Response: Thank you for this observation and suggestion. We are happy to consider your suggested revision by replacing efficacy with “mortality inducing effect on local mosquitoes”. The affected sentences have been revised to read as follows: “In Madagascar, bendiocarb was shown to have a mortality inducing effect on local mosquitoes of up to 80% for up to 5 months post spraying (12). Elsewhere in Zanzibar, a study to investigate the residual effect of pirimiphos methyl active ingredient sprayed on common surfaces of human dwellings showed that its mortality inducing effect on local mosquitoes was maintained on all sprayed surfaces up to 8 months post-IRS (13).” Lines 74-78.

One interesting part of your study is that your tested outcome is a clinical one. Testing IRS on mosquito mortality alone forces us to make an assumption about how reduced mosquitoes may lead to reduced clinical incidence. Your approach allows you to test the durability of the spray campaigns on the clinical outcome. I think this is worth highlighting, either as a sentence in the introduction or discussion.

Response: Thank you for this important suggestion. The following sentence has been added in the last paragraph of the introduction “The use of incidence data allows us to test the durability of the spray campaigns on the clinical outcome.” Lines 90-91.

It is also worth noting that malaria transmission in Uganda is mostly perennial, so even though there are slight seasonal peaks, transmission continues all year making the durability of the IRS product an important consideration. This helps understand the analysis too because the 6-month periods can be more consistently compared than would be the case in an area that had more seasonal transmission.

Response: This is a true observation. A sentence noting this has been added under the study setting as follows: “Malaria transmission in Uganda is mostly perennial, so even though there are slight seasonal peaks, transmission continues all year making the durability of the IRS product an important consideration.” Lines 103-105.

Discussion

Line 259 should be ‘concurs’ not ‘concur’

Response: This is done. The sentence now reads as follows: “However, for pirimiphos methyl, the results suggest that about one round per year is feasible and this concurs with the WHO recommendations(14).” See lines 263-265.

On line 264 perhaps say ‘may leave communities unprotected’ rather than may render IRS ineffective

Response: This is done. The sentence now reads as follows: “This implies that waiting for 7 months in case of bendiocarb before another round of IRS may leave communities unprotected.” See lines 268-269.

Lines 269- Perhaps say ‘due to the natural decline in transmission observed during these months of the year.

Response: This is done. The sentence now reads as follows: “For example, our results indicate higher transmission peak in May/June time, so perhaps the November spray rounds may have artificially bigger impacts due to the natural decline in transmission observed during these months of the year.” See lines 273-275.

On line 274 I would simply add – “but were not available.”

Response: This is done. The sentence now reads as follows: “Finer intervals like weekly data would help to improve our estimates but were not available.” See lines 278-279.

Perhaps add as a limitation that you assume no change in the level of insecticide resistance to either product throughout the time-series (if you have susceptibility bioassays for the region perhaps you could corroborate this assumption showing or referencing those).

Response: This is done by adding a fourth limitation as follows: “Fourth, we assume no change in the level of insecticide resistance to either product throughout the time-series. We did not have susceptibility bioassays for the study region to corroborate this assumption.” Lines 291-293.

I think there is a policy suggestion from your analysis that you could reasonably add to the discussion –

This work highlights well the variability in the length of time that the active ingredient deployed during a spray campaign may have, even for the same product within a single setting. Real-time surveillance of trends in clinical incidence could be used to help advise strategy teams on when to re-deploy the IRS intervention, ensuring rotation of products as guided by insecticide resistance management policies [you can ref WHO].

Response: Thank you so much for this suggested discussion. A paragraph has been added in the discussion as follows: “This work highlights well the variability in the length of time that the active ingredient deployed during a spray campaign may have, even for the same product within a single setting. Real-time surveillance of trends in clinical incidence could be used to help advise strategy teams on when to re-deploy the IRS intervention, ensuring rotation of products as guided by insecticide resistance management policies(14).” Lines 280-284.

The only other comment I have is that spray campaigns sometimes take months to deliver to a community, so perhaps it is worth noting the length of time, in these communities, that it took to complete delivery of each spray campaign and the ultimate percentage of households that were then covered in each spray round. Does this make a difference to the predictions?! Perhaps beyond the scope of the current paper as it is nearly ready to accept, but something to consider in future perhaps.

Response: A sentence showing length of time in months it took to complete each round of IRS is added under the study setting section as follows: “The length of time in months it took to complete delivery of each spray campaign were 1.9 for round 1, 1.3 for round 2, 0.8 for round 3, 1.0 for round 4, 2.5 for round 5, and 1.3 for round 6.” See lines 109-111. Unfortunately, we could not estimate the ultimate percentage of households that were covered because data on household listing were not available. Yes, it’s likely that the duration of completing each round of IRS could affect the prediction. This analysis will be considered in the future.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

8 Oct 2020

Estimating the Optimal Interval between Rounds of Indoor Residual Spraying of Insecticide Using Malaria Incidence Data from Cohort Studies

PONE-D-20-03773R3

Dear Dr. Mugenyi,

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David A. Larsen, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Well done!

Reviewers' comments:

14 Oct 2020

PONE-D-20-03773R3

Estimating the Optimal Interval between Rounds of Indoor Residual Spraying of Insecticide Using Malaria Incidence Data from Cohort Studies

Dear Dr. Mugenyi:

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