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Additional file 3: Table S1. Summary of total number of mosquitoes caught in the different collection sites in the longitudinal study. Table S2. Female Anopheles species collected in each sampling site in Puerto Princesa City, Palawan from May to December 2015.

Additional file 5: Table S1. Summary of total number of mosquitoes caught using the different trapping techniques in the trap comparison study. Table S2. Total number of female Anopheles mosquitoes collected in each trap and associated diversity indices (HLC – human landing catch, MBT – monkey-baited trap, HEN – human-baited electrocuting net, MEN – monkey-baited electrocuting net).

Additional file 4: Table S1. Summary of modeled coefficients for the nightly abundance of An. Balabacensis collected in each site in the longitudinal study from May to December 2015 (a – statistical difference relative to zero, b – statistical difference relative to reference category, * – indicates statistically significant difference). Table S2. Summary of modeled coefficients for the nightly abundance of An. flavirostris collected in the longitudinal study from May to December 2015 (a – statistical difference relative to zero, b – statistical difference relative to reference category, * – indicates statistically significant difference). Table S3. Summary of modeled coefficients for the hourly abundance of An. balabacensis collected in longitudinal study from May to December 2015 (a – statistical difference relative to zero, b – statistical difference relative to reference category, * – indicates statistically significant difference). Table S4. Summary of modeled coefficients for the hourly abundance of An. flavirostris collected in longitudinal study from May to December 2015 (a – statistical difference relative to zero, b – statistical difference relative to reference category, * – indicates statistically significant difference). Table S5. Summary of modeled coefficients for the nightly abundance of An. balabacensis collected in each trap (HLC – human landing catch, MBT – monkey-baited trap, HEN – human-baited electrocuting net, MEN – monkey-baited electrocuting net, a – statistical difference relative to zero, b – statistical difference relative to reference category, * – indicates statistically significant difference). Table S6. Summary of modeled coefficients for the nightly abundance of An. flavirostris collected in each trap (HLC – human landing catch, MBT – monkey-baited trap, HEN – human-baited electrocuting net, MEN – monkey-baited electrocuting net, a – statistical difference relative to zero, b – statistical difference relative to reference category, * – indicates statistically significant difference). Table S7. Summary of modeled coefficients for the nightly abundance of An. dispar collected in each trap (HLC – human landing catch, MBT – monkey-baited trap, HEN – human-baited electrocuting net, MEN – monkey-baited electrocuting net, a – statistical difference relative to zero, b – statistical difference relative to reference category, * – indicates statistically significant difference). Table S8. Summary of modeled coefficients for the nightly abundance of An. greeni collected in each trap (HLC – human landing catch, MBT – monkey-baited trap, HEN – human-baited electrocuting net, MEN – monkey-baited electrocuting net, a – statistical difference relative to zero, b – statistical difference relative to reference category, * – indicates statistically significant difference).

Additional file 1. Ethical considerations for the use of non-human primates.

Additional file 2. Detail of PCR primers used for detection of malaria parasite species in Anopheles mosquito specimens.

A number of primate census techniques have been developed over the past half-century, each of which have advantages and disadvantages in terms of resources required by researchers (e.g., time and costs), availability of technologies, and effectiveness in different habitat types. This study aims to explore the effectiveness of a thermal imaging technique to estimate the group size of different primate species populations in a degraded riparian forest in the Lower Kinabatangan Wildlife Sanctuary (LKWS), Sabah. We compared this survey technique to the conventional visual counting method along the riverbank. For 38 days, a total of 138 primate groups were observed by thermal camera and visually throughout the study. Optimal conditions for the thermal camera were clear weather, not more than 100 m distance from the observer to the targeted area, boat speed ranging between 5 and 12 km/h, and early morning between 04:30 and 05:30 am. The limitations of the thermal cameras include the inability to identify individual species, sexes, age classes, and also to discern between animals closely aggregated (i.e., mothers with attached infants). Despite these limitations with the thermal camera technique, 1.78 times more primates were detected than counting by eye (p

As malaria programmes move towards elimination, malaria transmission becomes increasingly spatially heterogenous, necessitating novel surveillance approaches to detect infections. Within Southeast Asia, forested landscapes are associated with both increased malaria transmission and reduced healthcare access. Here, we conduct health facility based surveys using tablet based applications and remote sensing data to geolocate and characterise environments of participant residences. Using a spatially explicit Bayesian process-based modelling approach, we quantified the detection probabilities of different surveillance approaches and estimated the distribution of malaria infections. We show that health facility surveys substantially increase spatial coverage of surveillance systems, with a probability of detection over three-fold greater than routine passive case detection (3.34, 95%BCI: 1.03, 8.27). Our results also demonstrate closed canopy forested areas have higher proportions of subpatent malaria infections only identified by molecular diagnostics. Applying these findings, we show risk-based surveillance approaches incorporating forest data provide a cost effective and operationally feasible method of identifying malaria foci.

Landscape changes have complex effects on malaria transmission, disrupting social and ecological systems determining the spatial distribution of risk. Within Southeast Asia, forested landscapes are associated with both increased malaria transmission and reduced healthcare access. Here, we adapt an ecological modelling framework to identify how local environmental factors influence the spatial distributions of malaria infections, diagnostic sensitivity and detection probabilities in the Philippines. Using convenience sampling of health facility attendees and Bayesian latent process models, we demonstrate how risk-based surveillance incorporating forest data increases the probability of detecting malaria foci over three-fold and enables estimation of underlying distributions of malaria infections. We show the sensitivity of routine diagnostics varies spatially, with the decreased sensitivity in closed canopy forest areas limiting the utility of passive reporting to identify spatial patterns of transmission. By adjusting for diagnostic sensitivity and targeting spatial coverage of health systems, we develop a model approach for how to use landscape data within disease surveillance systems. Together, this illustrates the essential role of environmental data in designing risk-based surveillance to provide an operationally feasible and cost-effective method to characterise malaria transmission while accounting for imperfect detection.

Additional file 1. Figure S1. Distribution of prevalence from sampled schools. Table S1. Monte Carlo maximum likelihood estimates for geostatistical model parameters for S. haematobium (a) and S. mansoni (b). Figure S2. Simulated country and distributions of districts, subdistricts and schools. Table S2. Consumable and equipment costs included for sampling 50 children using urine filtration (A) and Kato-Katz stool analysis (B); life expectancy and quantity needed per team is included for capital items. Table S3. Total survey mapping costs per school sampling 50 children. Table S4. Mean survey costs per school sampling 50 children by country, year and diagnostic method. (DOCX 950 kb)