The association between gold mining and malaria in Guyana: a statistical inference and time-series analysis.

BACKGROUND: Guyana reported a significant rise in malaria between 2008 and 2014. As there was no evidence of impairment of national malaria control strategies, public health authorities attributed the surge to a temporal increase in gold mining activity in forested regions. However, systematic analysis of this association is lacking because of the difficulties associated with collecting reliable data for both malaria and mining. We aimed to investigate the association between the international gold price and Plasmodium falciparum malaria transmission in Guyana between 2007 and 2019. We also aimed to evaluate the association between P falciparum cases and the El Niño-Southern Oscillation pattern, which has previously been suggested as a major driver of malaria.
METHODS: We used national malaria surveillance data from Guyana to estimate the correlation over time between the international gold price and reported P falciparum infections in individuals who were likely to be involved in mining activities (ie, men and boys aged between 15 and 50 years who were living in mining regions) for each month between 2007 and 2019. We compared the estimates with those obtained from individuals who were unlikely to be directly involved in mining activities (ie, women, children aged 12 years and younger, and adults aged over 70 years) and estimates obtained from individuals living in non-mining regions. We also evaluated the correlation between P falciparum infections and the El Niño-Southern Oscillation pattern in the same subpopulations and time period. Lastly, we evaluated the performance of a statistical model formulated to estimate P falciparum infections in real time using the international gold price as the predictor variable.
FINDINGS: The proportion of P falciparum malaria cases in temporary residents, which was used as a proxy for circulating individuals involved in gold mining, was highest during the years of peak gold price (ie, between 2008 and 2014). Cases of malaria in all demographic groups showed a strong positive correlation with the gold price, but only in regions with mining camps (0·88 [95% CI 0·84-0·89] for boys and men aged between 15 and 50 years and 0·80 [0·73-0·85] for the aggregated population of women, children aged 12 years and younger, and adults older than 70 years). The highest correlation occurred earlier in men and boys aged between 15 and 50 years, the demographic most likely to be miners, suggesting that transmission in mining camps is followed by infections in the community. On the basis of these findings, we were able to reliably forecast P falciparum malaria trends using only the gold price as the predictor variable. A 1% increase in gold price was associated with a 2·13% increase in P falciparum infections after 1 month in the mining populations, and with a 1·63% increase after 2 months in the non-mining populations. Lastly, La Niña climatic events showed an additional, smaller positive correlation with malaria transmission.
INTERPRETATION: Our analysis provides evidence that the P falciparum malaria surge observed in Guyana between 2008 and 2014 was likely to have been driven mainly by an increase in gold mining, while climate factors might have contributed synergistically. We propose that the international gold price over time is a useful indicator of malaria trends. We conclude that the feasibility of malaria elimination in Guyana, and in other areas in the Amazon where malaria and gold mining overlap, should be evaluated against the challenges posed by rapidly rising gold prices. FUNDING: Ramón Areces Foundation, National Institutes of Health, and National Institute of General Medical Sciences.

Introduction

The Guiana Shield region of the Amazon basin represents one the most challenging areas for malaria surveillance and control. Malaria-endemic populations are often remote and highly mobile. These populations frequently do not have access to public health care and prefer to buy antimalarials on the private market, where poor quality drugs are prevalent. Further, financial resources are scarce and vector control is challenging for Amazonian Anopheline vector species in these settings due to their feeding behaviour and ecology; for example, Anopheles darlingi, which is considered to be the major vector in the Amazonian basin, is both a generalist and opportunistic species that can feed indoors and outdoors. All of these challenges can preclude prospects of malaria elimination in the region. Guyana in particular has been identified as one of the countries in the Americas that might struggle to achieve the elimination goals set up by WHO for 2030.1, 3

Evidence before this study

We searched within our personal libraries, and within PubMed and Google Scholar for articles published up until December, 2020, using the search terms “gold”, “mining”, and “malaria” for manuscripts addressing the association between gold mining and malaria. Qualitative and quantitative research articles and reviews published in English, Portugese, or Spanish that evaluated the relationship between gold mining and malaria were included. We found that the association in the Amazon region has been known anecdotally since the 1980s, but the remote and transient nature of mining activities has led to difficulties in quantifying this association. Furthermore, frameworks intended to predict malaria trends have focused on environmental variables rather than economic drivers, despite the clear link between labour migration and malaria epidemiology.

Added value of this study

Here we analysed the association between international gold price and malaria cases in Guyana. This study included all Plasmodium falciparum infections with detailed epidemiological and demographic data reported spanning 13 years of national surveillance in Guyana (2007–19); during that period, a significant increase of malaria transmission in the country was observed in the context of a well recognised national gold rush. We used international gold price, which is publicly available in real time, to develop a causal inference framework and showed that gold mining was likely to be the major driver of malaria cases. We identify epidemiological characteristics consistent with frontier malaria in other settings, such as a clear association with deforestation, mobility, and men of working age.

Implications of the available evidence

Our study showed that in Guyana, a paradigmatic example of malaria in the Amazon region, gold mining was a major driver of malaria transmission at a national level. This finding, together with our finding that mining-related transmission spills over into surrounding communities, is a crucial consideration for redesigning and prioritising control and elimination strategies in the Guiana Shield and beyond. Finally, we propose that gold price dynamics are a useful predictor for forecasting malaria transmission for remote mining regions where traditional surveillance is operationally challenging.

Malaria transmission in Guyana showed an increasing trend followed by a subsequent decrease between 2008 and 2014; a trend that was not reported at the country level in neighbouring countries Brazil, French Guiana, Suriname, and Venezuela. This observation prompted a public health investigation which, in the absence of changes to interventions such as case management, bed net distribution, and other constraints within the country's malaria control strategy, attributed the increase to the rise of gold mining activities. The association between mining and malaria has been previously observed throughout the Amazon basin, including in Brazil, Colombia, French Guiana, Peru, and Venezuela6, 7, 8, 9, 10 and malaria has long been regarded as an occupational hazard in mining communities because it is one of the leading morbidities in miners.11, 12 However, the association between gold mining and malaria transmission in the region has not been systematically evaluated, largely because of the scarcity of data. Alternatively, climatic factors, in particular the El Niño-Southern Oscillation (ENSO), have been previously proposed as drivers of malaria in the Guiana Shield.13, 14 The underlying mechanism linking climate and malaria is thought to be an increase in the mosquito reproduction, survival, and overall transmission capacity under favourable weather conditions.15, 16, 17

Having a detailed understanding of the association between malaria transmission and gold mining in the Guiana Shield is crucial for designing successful interventions within the elimination framework: if mining settings are the major source of infection exposure, control and prevention strategies need to be targeted in those populations. Overall, discerning the drivers of malaria is key to the development of evidence-based strategies for elimination, not only because interventions must reflect local drivers of transmission but also to develop meaningful predictive indicators that can be used in monitoring and planning. Real-time monitoring of the anthropogenic impact on the Amazonian frontier, such as spatial-temporal mapping of deforested areas using satellite images or highly granular population mobility analysis, could give invaluable information for malaria prevention and control. However, these approaches require resourcing and planning that are currently lacking and might compete with the already limited funds available for malaria programmes. Conversely, the international gold price is publicly available in real time, and could be a key malaria indicator because of its importance in driving prospective mining activity, especially for small-to-medium scale camps, which have been strongly linked to malaria transmission. Indeed, in the Guiana Shield, periods when high gold prices are reached for a sustained time are known to lead to so-called gold rush migration, in which a substantial proportion of the population temporarily migrates to mining regions for work, often in fragile labour conditions.

In this study, we aimed to test the hypothesis that the gold price can be used as a proxy of gold mining to understand its association with malaria transmission in Guyana. We aimed to evaluate the correlation between the international gold price and Plasmodium falciparum malaria cases in different population groups and regions. Furthermore, on the basis of previous evidence relating malaria and La Niña climate events in Guyana, we aimed to do similar analyses using the Southern Oscillation Index (SOI) as a counterfactual driver of transmission. In addition, we aimed to evaluate the performance of a generalised linear model to forecast malaria cases using the gold price as a predictor.

Methods

Data collection

Guyana is divided into ten administrative regions, five of which (regions 1, 7–10) harbour most malaria cases. The number of reported cases in the other regions (regions 2–6) is low, particularly on the coast, and are almost absent in region 3 and region 4 (where the capital, Georgetown, is located). The malaria dataset analysed in this study comprised all P falciparum episodes reported through passive surveillance to the Malaria Program at the Vector Control Services (Ministry of Public Health) between 2007 and 2019. Malaria episodes were defined using both medical diagnoses and parasitological tests done on peripheral blood samples (through direct microscopy or through rapid diagnostic tests). It is estimated that reported cases of malaria represent around 55% of total cases. We could not calculate incidence rates because of the scarcity of data for the population at risk, which largely comprised mobile populations. Information included demographics (ie, age and sex), date and location of diagnosis, and probable location of infection. Monthly average gold price was obtained using historical prices from Quandl. For the main analysis, we adjusted the gold price time series to expected increases in the cost of mining operationalisation by extracting the deterministic trend (appendix pp 1–2).

The Ministry of Public Health, Guyana, granted access to non-identifiable surveillance data obtained from the Malaria Program (Vector Control Services) under Institutional Review Board exemption. Further, Institutional Review Board exemption was obtained from Harvard School of Public Health (Boston, MA, USA), Human Research Protection Program, protocol number IRB18-1638.

Malaria in mobile mining populations and gold price

We assumed that individuals involved in gold mining were primarily men and boys aged between 15 and 50 years. We predicted that this group would include both resident miners (ie, individuals engaged in mining activity in their region of residence) and circulating miners (ie, individuals who temporarily migrate to a region to engage in mining activities). Further, we computed the monthly proportion of malaria cases in men and boys aged between 15 and 50 years who were infected in any of the regions with a high number of mining camps (ie, regions 1, 7, 8, and 10) but reported residence in a different region. Although this categorisation was not perfect, it roughly approximated mining-related infections in the circulating population. We then evaluated the monthly proportion of malaria cases in men and boys aged between 15 and 50 years who were infected in any of the regions with a high number of mining camps, but who reported residence in a different region, conditional to the overall annual gold price, which we then ranked in quartiles.

Statistical analysis

To analyse the association between the P falciparum malaria epidemiological curve and gold price, we first computed the cross-correlation function coefficients between the gold price and reported cases in men and boys aged between 15 and 50 years in regions with a high number of mining camps (ie, regions 1, 7, 8, and 10) at different month lags, ranging 24 months before and after recorded episodes of malaria. We assumed that this population mostly represented transmission directly occurring in mining camps. For the shift showing the highest cross-correlation coefficient, we computed the Pearson product-moment correlation coefficient with 95% CIs. We further compared the cross-correlation to that calculated using infections in women, children aged 12 years and younger, and adults older than 70 years reported in the same regions, which we assumed represented transmission occurring in non-miners in the same regions.

Furthermore, we did the analysis for the same groups, but this time using infections reported in regions with no mining camps or low numbers of mining camps (ie, regions 2, 5, 6, and 9), assuming these groups represented transmission unrelated to gold mining. In addition, we assessed if the spectrum of the P falciparum malaria time series changed over time and how these data related to the frequency domain of gold price (appendix p 6). Using the same approach as with P falciparum cases, we also assessed the correlation between the gold price and Plasmodium vivax cases. Lastly, we took a similar approach using the SOI to evaluate the association between ENSO and malaria (appendix p 4).

Finally, we evaluated whether the gold price was a reliable predictor of future malaria cases. Briefly, we implemented a generalised linear model in which expected monthly malaria case counts could be modelled as a negative binomial distribution with a mean parameter (μt) and a dispersion parameter (θ), which both depended on the natural logarithm of the monthly average gold price. For each year between 2008 and 2019, we first computed μt and θ using maximum likelihood estimates from the previous years, calculated the 95% and 50% prediction intervals of all P falciparum cases nationwide using a bootstrapping procedure. We then compared the trend and prediction intervals with true observations. For the sensitivity analysis, we included SOI as a predictor and used the model to predict cases in non-mining regions. We also used an alternative model assuming truncated normal distribution (appendix pp 7–8).

Role of the funding source

The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Results

During the study period, a total of 130 242 P falciparum infections were reported in Guyana, of which 88 590 (68%) occurred in men and boys aged between 15 to 50 years, who we assumed were more representative of individuals involved in gold mining than other demographics. Reported P falciparum cases by month mirrored adjusted gold price trends for the period of study (figure 1A), with a marked upward increase from 2008 to 2012 followed by a decrease between 2013 and 2015, and subsequently reaching a steady state until 2019. Deforestation due to mining also followed similar trends (appendix p 2). Distribution of P falciparum cases varied by age and sex, and were significantly higher in men (figure 1B). The highest proportions of monthly malaria cases in non-resident men and boys aged between 15 and 50 years from total, which approximately represent the mining population circulating inter-regionally, were observed in the years in the upper quartile of adjusted gold price (figure 1C). The mean proportion of monthly malaria cases (in non-resident men and boys aged between 15 and 50 years from total) calculated from the periods ranked by gold price quartile increased continuously with higher prices, while the monthly proportion of cases showed wider dispersion in the second and third quartiles compared with the first and fourth quartiles.

Figure 1

Distribution of Plasmodium falciparum cases and gold price in Guyana, 2007–19

(A) Time series of P falciparum malaria cases in Guyana by month (grey) and detrended gold price per month (yellow). (B) Distribution of P falciparum cases between 2007 and 2019 by age group and sex in mining regions (regions 1, 7, 8, and 10). (C) Monthly proportion of malaria cases (represented by red points) in non-resident men and boys in gold mining regions aged between 15 and 50 years by gold price quartile rank. Boxplots represent mean and range between the 2·5th and 97·5th percentiles.

The adjusted gold price correlated significantly with P falciparum malaria case counts in mining regions (figure 2), in particular in the groups that were most representative of the mining population (ie, men and boys aged between 15 and 50 years), as shown in figure 2A and 2C. The strongest cross-correlation was obtained with a 1-month shift of the gold price; the computed correlation coefficient using 1-month shift was 0·88 (p<0·0001 [95% CI 0·84–0·89]). The correlation was also significant in people who were non-representative of the mining population (figure 2A, 2C), but the highest correlation for this group was computed with a 2-month shift, which resulted in a coefficient value of 0·80 (p<0·0001 [0·73–0·85]). The gold price did not correlate with cases in non-mining regions, either when using total cases or disaggregating by populations (figure 2B). Further, a similar pattern was observed regarding P vivax, with cases in men and boys aged between 15 and 50 years showing the highest correlation compared with people representing the non-mining population. In this instance, the highest coefficient was obtained with a longer lag of 3 months, which was consistent with a less predictable incubation period observed in P vivax infections (appendix p 3).

Figure 2

Stratified correlations between Plasmodium falciparum malaria and gold price time series in Guyana, 2007–19

(A) Number of P falciparum cases in mining regions (regions 1, 7, 8, and 10) disaggregated by mining populations (shown in green) and non-mining populations (shown in brown). (B) Case time series in non-mining regions (regions 2, 5, 6, and 9) disaggregated by mining populations (shown in green) and non-mining populations (shown in brown). (C) Scatter plot showing monthly cases in mining populations (green dots) with a 1-month shift and non-mining populations (red dots) in mining regions versus monthly adjusted gold price with a 2-months shift.

Regarding the ENSO, the SOI also correlated with malaria cases in mining regions, though the correlation was weaker than that computed using the gold price (appendix p 4) and was absent when computed using case counts in non-mining regions. The highest correlation between SOI and P falciparum in the mining population was found when using a 13-months shift of the SOI time series. Regarding cases in the non-mining population, the highest correlation was obtained using a 2-months shift (appendix p 4).

Lastly, the proposed regression model that used only the gold price as a predictor was capable of reliably capturing the trajectory of the malaria epidemiological curve (figure 3), with the characteristic upward drift followed by downward trend between 2009 and 2015, followed by a steady state. Simply, our model showed that, other factors being equal, an increase of 1% in the adjusted gold price led to a 2·13% (SE 0·12%) increase in P falciparum infections in the mining population 1 month later, and a 1·63% (0·13%) increase in the non-mining population 2 months later. Reported cases were within 95% CI predictions, except some outliers between 2008 and 2010, and in 2014. The discrepancy for the early years might reflect insufficient information to accurately calculate the regression coefficients. Further, the discrepancy in 2014 might reflect model misspecification, either because the association between decreasing gold price and cases of malaria was not linear, or because transmission was affected by a significant increase in bed net distribution (and subsequent use) implemented in 2014 (appendix pp 7–8). In a sensitivity analysis, we showed that the model could not recover trends of cases in non-mining regions. Furthermore, we showed that using SOI as predictor value did not improve the accuracy of the model predictions. Assuming a truncated normal distribution reduced the dispersion of the prediction uncertainty in particular for the highest values but presented similar limitations as the main analysis (appendix p 8).

Figure 3

Model forecasting Plasmodium falciparum infections in Guyana 2008 and 2019 using the gold price as the predictor variable

Prediction intervals of expected number of P falciparum malaria cases per month in Guyana, estimated by fitting a generalised linear model that used only gold price as the predictor. Ribbons represent 25th–75th percentiles (shown in dark green) and 2·5th and 97·5th percentiles (shown in light green). Lines represent true reported cases. Red area shows the period (2014) when the bed net distribution was strengthened (appendix p 8).

Discussion

In this statistical inference and time series analysis, we evaluated the association between gold price dynamics and P falciparum malaria epidemiology in Guyana between the years 2007 and 2019. First, we found that the proportion of cases of malaria in mobile populations in mining regions escalated during the years when the gold price peaked. This finding probably reflected the circulation of individuals working in mines in the context of a major gold rush and a subsequent increase of malaria among them. In addition, most P falciparum infections in Guyana occurred in the mining regions, and therefore individuals moving temporarily from malaria-free-settings could have increased overall transmission by reintroducing malaria when returning if the vector was present. Furthermore, these individuals could have contributed to increased numbers of clinical cases due to lower immunity than people who permanently live in these areas and hence the higher observed proportion of symptomatic infections.

Second, we found a significant correlation between the gold price and P falciparum infections, but only in regions with mining activity. Lastly, during years of sustained increase, the number of cases of P falciparum malaria mirrored periodic oscillations of the gold price (appendix pp 5–6). In the mining population, a 1-month delay between gold price increase and rise in cases of malaria was observed, which was consistent with the times required by mobility constraints and the vector and disease processes; the longer delay of 2 months found in the non-mining population, together with a smaller correlation, suggested that malaria in the non-mining communities reflected a spillover of transmission in miners. As a counterfactual, no correlation was found between gold price and P falciparum malaria in non-mining regions.

Additionally, we found a smaller but significant correlation between P falciparum malaria and a 0 year shifted SOI (ie, no shift) in the non-mining population and a 1 year shifted SOI, in particular with La Niña events, in the mining population. The effect of climate forcing has been mostly attributed to favouring the overall transmission capacity of the vector due to increased reproduction and survival. In Guyana, a 0 to 1 year correlation between malaria and El Niño events has been shown during the first years after the resurgence of malaria following the eradication in 1975 when transmission was very low, while a 0 to 1 year correlation between La Niña and malaria was observed in the neighbouring Venezuela between 1957–97, with moderate to high transmission, consistent with our findings and current malaria burden in Guyana. Our findings confirm the association between climate and malaria transmission in Guyana previously described, but our analysis suggests that its impact is overshadowed by a major surge of gold mining activity.

Our study has several limitations. First, our analysis aimed to infer causality from observations, which requires cautious interpretation as we were unable to disentangle the mechanistic pathways linking gold prices to malaria infections. We also assumed that gold prices are a proxy of gold mining activity. Our motivation for the use of gold prices began with an absence of available data that could support the analysis of validated pathways relating to mining and malaria, and thus needs confirmation in further studies. Nevertheless, our analysis is consistent with the existing theoretical framework of frontier malaria26, 27 in both our analysis on population mobility linked to gold price (figure 1) and deforestation (appendix p 2).

Second, because we did not have information on the exposure relative to mining camps (ie, infection during mining vs infection in the community), we assumed that the population of men and boys aged between 15 and 50 years approximated the mining population, but this is unlikely to be a perfect proxy. A large proportion of men and boys aged between 15 and 50 years would have been exposed outside of mining camps, but also women have been reported to have an increasing presence in mining settings. However, our analysis, and particularly the different correlation coefficients and delays between gold price and mining compared with community cases, suggests that this disaggregation is capable of capturing the differential transmission dynamics. Further, a proportion of individuals involved in non-legal mining activities might not seek medical attention and therefore could have remained out of the surveillance system. Although in Guyana there is universal access to diagnostics and treatment of malaria, the exclusion of a large proportion of malaria cases in miners would lead to an underestimation of gold mining related transmission in our analysis. Also, we did not adjust malaria cases to the population at risk because this population largely comprised individuals who were mobile. However, given the small growth rate estimates for the study period (0·32% per year on average, obtained from census projections), we believe that this adjustment would not have changed our overall results and interpretation.

Third, in our main analysis we did not adjust to potential confounders, such as the influence of the SOI, changes in other diseases, or changes on deployed interventions. Although this decision might have affected the accuracy of the correlation between gold price dynamics and subsequent mining activities as a malaria driver, we believe that the overall inference framework supports our main conclusion that gold mining was a major driver of the observed increased transmission between 2008 and 2014. Further, our proposed forecasting model that used gold price as predictor, although possibly affected by misspecifications because of its statistical rather than mechanistic background, was still a useful surveillance tool as it was capable of providing actionable information, such as case count trends. Of note, including the SOI did not seem to improve our predictions (appendix pp 4, 7–8), which can be interpreted as a reduction of climate forcing under strong influence of mining on malaria transmission.

More evidence is needed to establish definitive causation between mining and malaria transmission, as well as for a more detailed mechanistic understanding of the link between these. Worth noting, this challenge might not only relate to Guyana but to the whole Guiana Shield, and other areas in the Amazonian region where there is an insufficient capacity to control and eliminate malaria. Spatial-temporal prospective mapping of deforested areas or highly granular population mobility analysis, coupled with epidemiological and molecular surveillance, could improve our understanding of malaria transmission through the region and support elimination efforts. Interestingly, neighbouring countries Suriname and French Guiana have reported an overall decrease of malaria despite having similar challenges to gold mining.6, 32 However, it is likely that the strong efforts on control interventions have been very successful in reducing transmission during recent years in both countries. Conversely, there is evidence of a similar association between gold mining and malaria in Brazil, Venezuela, Colombia, and Peru, which aligns well with our findings. Particularly relevant is the example of Venezuela, where the collapse of the health system and increase in malaria is well documented and has been linked to gold mining.

Our work provides evidence that malaria transmission in Guyana is strongly linked to gold mining. Although this finding has been reported at subnational level in other countries,6, 7, 8, 9 the particular case of Guyana suggests that mining settings should be key targets when designing malaria elimination strategies. We also provide evidence that both deforestation and population mobility are correlated with gold price dynamics. Deforestation has been linked to enhancing vector survival by improving breeding conditions and access to humans for blood meals, which can be particularly relevant in mining camps. It is important to highlight that in this case, any deforestation activity not necessarily linked to mining might influence malaria and our analysis could be confounded by a correlation between gold price and other commodities related to forest work. Regarding mobility, it is likely that increased mobility facilitates the exposure of non-immune individuals. Both issues have important implications for designing future public health interventions. Some of such potential actions have already shown to be effective in gold mining settings, but it is likely that malaria control in these settings remain a public health gap because of remoteness, low levels of access to health, and the issue that a large proportion of the mining activity is illegal.36, 37

The importance of gold mining as a driver of transmission remains beyond targeting the mining areas themselves; our findings suggest that malaria transmission linked to mining might spillover into the community, and we therefore suggest that targeting miners and mining regions could have a disproportionate impact on country-wide malaria control efforts. Again, further research needs to address the interrelation between both environments. Meanwhile, strong policies on interventions need to be urgently developed and implemented in the mining setting to guarantee control in miners and further spread to the community for both P falciparum and P vivax infections, particularly given the detection of artemisinin resistant clones and the Venezuelan health crisis. These necessary actions will likely require coordination beyond public health, and the inclusion of stakeholders responsible for the regulation, administration, and operationalisation of mining activities.

The international gold price is available in real time, with numbers of cases of malaria following its trend with a 1–2 month delay. We propose that gold price is, therefore, a useful indicator of future malaria cases in the country. Forecasting can provide important preliminary information, such as on overall trends (increasing or non-increasing relative to the past year) and disease estimates, which in turn can facilitate key decisions, particularly during unexpected increases of disease transmission, such as: (1) whether intervention efforts, such as vector control or bed net distribution, need to be extended, (2) ensuring that sufficient case-management resources are available in all endemic areas (ie, diagnostic tools, including rapid diagnostic tests and available treatment) and, (3) redesigning resource allocation within the overall public health programmes. These actions would allow timely control response, especially in mining regions with very long reporting delays that compromise the timeliness of data-driven public health decisions and responses. We conclude that rising gold prices and subsequent mining represent a large risk to the feasibility of malaria control and malaria elimination strategies in Guyana, with similar implications to other locations in the Amazonian region.

Declaration of interests

We declare no competing interests.