| Literature DB >> 29183370 |
Sai Thein Than Tun1, Lorenz von Seidlein2,3, Tiengkham Pongvongsa4, Mayfong Mayxay3,5,6, Sompob Saralamba2, Shwe Sin Kyaw2, Phetsavanh Chanthavilay6,7, Olivier Celhay2, Tran Dang Nguyen8, Thu Nguyen-Anh Tran8, Daniel M Parker9, Maciej F Boni3,8,10, Arjen M Dondorp2,3, Lisa J White2,3.
Abstract
BACKGROUND: The number of Plasmodium falciparum malaria cases around the world has decreased substantially over the last 15 years, but with the spread of resistance against anti-malarial drugs and insecticides, this decline may not continue. There is an urgent need to consider alternative, accelerated strategies to eliminate malaria in countries like Lao PDR, where there are a few remaining endemic areas. A deterministic compartmental modelling tool was used to develop an integrated strategy for P. falciparum elimination in the Savannakhet province of Lao PDR. The model was designed to include key aspects of malaria transmission and integrated control measures, along with a user-friendly interface.Entities:
Keywords: Laos; Malaria elimination; Malaria surveillance; Mass Vaccination and Drug Administration (MVDA); Mathematical model; Plasmodium falciparum; Savannakhet
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Year: 2017 PMID: 29183370 PMCID: PMC5706414 DOI: 10.1186/s12936-017-2130-3
Source DB: PubMed Journal: Malar J ISSN: 1475-2875 Impact factor: 2.979
Fig. 1Savannakhet Province in Lao People’s Democratic Republic
Fig. 2Timeline for Mass Vaccination and Drug Administration (MVDA), booster administration, and follow-up
Fig. 3Model structure
Fig. 4Dynamics of the model
Fig. 5Interpretation of the monthly incidence of malaria from the output of the modelling tool. The incidence is indicated as a range, in which the lower border indicates the estimated incidence of P. falciparum detected by passive surveillance, and the upper border the estimated incidence of all infections. Interruption of transmission is achieved when the incidence no longer reaches the elimination threshold
Fig. 6Interpretation of the prevalence of malaria from the output of the modelling tool
Assumptions for the baseline scenario in Savannakhet
| Parameter | Value | Unit |
|---|---|---|
| Baseline API | 10 | Per 1000 per year |
| Number of mosquito bites per human per night (peak season) | 20 | Per night per human |
| % of all infections that are caught outside the village (forest) | 30 | % |
| Baseline % of all clinical cases treated | 25 | % |
| Baseline coverage of LLIN | 70 | % |
| % of infections averted due to ownership of LLIN | 30 | % |
| Baseline coverage of IRS | 0 | % |
| % reduction in biting rate due to IRS | 15 | % |
| Imported clinical cases | 1 | Per 1000 per year |
| Imported asymptomatic microscopically detectable carriers | 1 | Per 1000 per year |
| Imported asymptomatic microscopically undetectable carriers | 1 | Per 1000 per year |
| % of cases failing treatment in 2018 and before | 5 | % |
| % of cases failing treatment in 2019 | 15 | % |
| % of cases failing treatment in 2020 and after | 30 | % |
These parameters are set up in the “Baseline” tab in the modelling tool
Assumptions for the increasing coverage of LLIN and EDAT (universal coverage scenario)
| Parameter | Value | Unit |
|---|---|---|
| Years to scale up EDAT | 1 | Year |
| New % of all clinical cases treated | 70 | % |
| Years to universal access to LLIN | 1 | Year |
| New bed net use of LLIN | 90 | % |
These parameters are from the “Interventions currently available” tab in the modelling tool
Assumptions for the focal MDA in addition to universal coverage scenario in Table 2
| Parameter | Value | Unit |
|---|---|---|
| Effective population coverage (entire province) of focal MVDA in round 1 | 50 | % |
| Effective population coverage (entire province) of focal MVDA in round 2 | 50 | % |
| Effective population coverage of focal MVDA in round 3 | 50 | % |
| Timing of 1st round | September 2018 | |
| Timing of 2nd round | October 2018 | |
| Timing of 3rd round | November 2018 | |
| Months to complete each round | 6 | Months |
| Days prophylaxis provided by the ACT | 30 | Days |
These parameters are from the “Interventions under trial: Focal MVDA (hotspot)” tab in the modelling tool
Assumptions for the MVDA in addition to those in Tables 2 and 3
| Parameter | Value | Unit |
|---|---|---|
| % protective efficacy of RTS,S with 1st dose | 75 | % |
| % protective efficacy of RTS,S with 2nd dose | 80 | % |
| % protective efficacy of RTS,S with 3rd dose | 92 | % |
| Half-life of vaccine protection | 90 | days |
These parameters are from the “Interventions under trial: Focal MVDA (hotspot)” tab in the modelling tool
Assumptions for the mass screening and treatment of imported cases in addition to Tables 2, 3 and 4
| Parameter | Value | Unit |
|---|---|---|
| Years to scale up MSAT | 1 | Years |
| New coverage of MSAT | 90 | % |
| Sensitivity HS RDT (clinical infections) | 99 | % |
| Sensitivity HS RDT (asymptomatic, microscopically detectable) | 87 | % |
| Sensitivity HS RDT (asymptomatic, microscopically undetectable) | 44 | % |
These parameters are from the “Interventions under trial: Focal MSAT (mobile)” tab in the modelling tool
Fig. 7The baseline estimates illustrate the seasonal variability in the presence of spreading anti-malarial resistance
Fig. 8Increasing the coverage of community health workers who distribute LLIN and provide EDAT
Fig. 9Addition of mass drug administrations (MDA)
Fig. 10Addition of vaccinations to MDA (MVDA)
Fig. 11Stopping the importation of infection through screening and treatment of visitors and immigrants