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BackgroundSchistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas. Methodology/Principal findingsIn the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential forS. mansoniinfection. Landscape connectivity metrics were also analysed. Conclusions/SignificanceThis study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region’s low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination. 

Mosquito vectors of pathogens (e.g.,Aedes,Anopheles, andCulexspp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change. 

West Nile virus (WNV) is the leading mosquito-borne disease causing-pathogen in the United States. Concerningly, there are no prophylactics or drug treatments for WNV and public health programs rely heavily on vector control efforts to lessen disease incidence. Insecticides can be effective in reducing vector numbers if implemented strategically, but can diminish in efficacy and promote insecticide resistance otherwise. Vector control programs which employ mass-fogging applications of insecticides, often conduct these methods during the late-night hours, when diel temperatures are coldest, and without a-priori knowledge on daily mosquito activity patterns. This study’s aims were to 1) quantify the effect of temperature on the toxicity of two conventional insecticides used in fogging applications (malathion and deltamethrin) toCulex tarsalis, an important WNV vector, and 2) quantify the time of host-seeking ofCx.tarsalisand other local mosquito species in Maricopa County, Arizona. The temperature-toxicity relationship of insecticides was assessed using the WHO tube bioassay, and adultCx.tarsalis, collected as larvae, were exposed to three different insecticide doses at three temperature regimes (15, 25, and 35°C; 80% RH). Time of host-seeking was assessed using collection bottle rotators with encephalitis vector survey traps baited with dry ice, first at 3h intervals during a full day, followed by 1h intervals during the night-time. Malathion became less toxic at cooler temperatures at all doses, while deltamethrin was less toxic at cooler temperatures at the low dose. Regarding time of host-seeking,Cx.tarsalis,Aedes vexans, andCulex quinquefasciatuswere the most abundant vectors captured. During the 3-hour interval surveillance over a full day,Cx.tarsaliswere most-active during post-midnight biting (00:00–6:00), accounting for 69.0% of allCx.tarsalis, while pre-midnight biting (18:00–24:00) accounted for 30.0% ofCx.tarsalis. During the 1-hour interval surveillance overnight,Cx.tarsaliswere most-active during pre-midnight hours (18:00–24:00), accounting for 50.2% ofCx.tarsaliscaptures, while post-midnight biting (00:00–6:00) accounted for 49.8% ofCx.tarsalis. Our results suggest that programs employing large-scale applications of insecticidal fogging should consider temperature-toxicity relationships coupled with time of host-seeking data to maximize the efficacy of vector control interventions in reducing mosquito-borne disease burden.