Appendix B. Simulation results from the individually fitted models.

Simulation results from the individually fitted models

Appendix A. Description of parameter-estimation method.

Description of parameter-estimation method

Sharp decrease in infection incidence between 2007 and 2008.

<p>Median (black line) and 95% credible interval (grey polygon) for the yearly parameters . A horizontal line at zero is drawn for reference (grey line) and numbers refer to calendar years.</p

Ignoring the influence of observations on disease dynamics results in misleading inference on infection incidence trends.

<p>True infection prevalence for each county (Acrelandia – AC, Placido de Castro – PC, and Senador Guiomard – SG) is depicted in the top six panels (black lines), together with the estimated 95% credible interval for infection prevalence (red polygons). The bottom panels depict the true and inferred infection incidence (black lines and red polygons, respectively). Because simulations and the fitted models assume that the three counties have the same infection incidence, incidence results are displayed in a single panel. Left and right panels show results from the alternative model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003312#pcbi.1003312.e107" target="_blank">eqn. 10a</a>) and original model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003312#pcbi.1003312.e059" target="_blank">eqn. 10</a>), respectively.</p

Conservation Efforts May Increase Malaria Burden in the Brazilian Amazon

<div><p>Background</p><p>Large-scale forest conservation projects are underway in the Brazilian Amazon but little is known regarding their public health impact. Current literature emphasizes how land clearing increases malaria incidence, leading to the conclusion that forest conservation decreases malaria burden. Yet, there is also evidence that proximity to forest fringes increases malaria incidence, which implies the <i>opposite</i> relationship between forest conservation and malaria. We compare the effect of these environmental factors on malaria and explore its implications.</p> <p>Methods and Findings</p><p>Using a large malaria dataset (∼1,300,000 positive malaria tests collected over ∼4.5 million km²), satellite imagery, permutation tests, and hierarchical Bayesian regressions, we show that greater forest cover (as a proxy for proximity to forest fringes) tends to be associated with higher malaria incidence, and that forest cover effect was 25 times greater than the land clearing effect, the often cited culprit of malaria in the region. These findings have important implications for land use/land cover (LULC) policies in the region. We find that cities close to protected areas (PA’s) tend to have higher malaria incidence than cities far from PA’s. Using future LULC scenarios, we show that avoiding 10% of deforestation through better governance might result in an average 2-fold increase in malaria incidence by 2050 in urban health posts.</p> <p>Conclusions</p><p>Our results suggest that cost analysis of reduced carbon emissions from conservation efforts in the region should account for increased malaria morbidity, and that conservation initiatives should consider adopting malaria mitigation strategies. Coordinated actions from disparate science fields, government ministries, and global initiatives (e.g., Reduced Emissions from Deforestation and Degradation; Millenium Development Goals; Roll Back Malaria; and Global Fund to Fight AIDS, Tuberculosis and Malaria), will be required to decrease malaria toll in the region while preserving these important ecosystems.</p> </div

Informative priors used for the observation model parameters.

<p>Description of the individual-level data (original successes and trials) and the resulting informative prior parameters.</p

Temporal and geographical distribution of the government surveillance malaria data.

<p>Malaria data depiction for Acrelandia (AC), Placido de Castro (PC), and Senador Guiomard (SG) counties (black, blue, and red lines, respectively). Number of positive exams and total number of exams are shown in upper and lower panels, respectively.</p

Malaria incidence is higher in areas with more forest cover whereas no clear pattern arises regarding deforestation rates.

<p>Upper panels: Data were stratified into 10 percentile population size classes and average number of malaria cases per month for each year and city was depicted. Within each size class, we compare cities with high (green box-plots) vs. low forest cover (white box-plots) (upper left panel); and cities with high (grey box-plots ) vs. low deforestation rate (white box-plots) (upper right panel). Cities with high forest cover (or high deforestation rates) are cities that have forest cover (or deforestation rate) higher than the median for that size class. ‘n.s’, ‘*’, ‘**’, and ‘***’ are non-significant (p>0.05), significant (0.01</p

Prior relationship between detection probability given sampled and infection prevalence.

<p>Approximate relationship between detection probability given that the person was sampled by the government surveillance system and infection prevalence , based on informative priors on the parameters of the observation model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003312#pcbi-1003312-t002" target="_blank">Table 2</a>). Solid and dashed lines are the median and 95% prior credible intervals based on the original (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003312#pcbi.1003312.e026" target="_blank">eqn. 6</a>, black lines) and simplified (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003312#pcbi.1003312.e031" target="_blank">eqn. 7</a>, red lines) observation models.</p

Out-of-sample predictive ability of the proposed model.

<p>Comparison of observed vs. predicted number of positive malaria exams. A 1∶1 line was added for reference (dashed red line). Different colors indicate different counties (AC = Acrelandia, PC = Placido de Castro, and SG = Senador Guiomard).</p