Fires were historically rare in tropical forests of West and Central Africa, where dense vegetation, rapid decomposition, and high moisture limit available fuels. However, increasing heat and drought combined with forest degradation and fragmentation are making these areas more susceptible to wildfire. We evaluated historical patterns of MODIS active fires in African tropical forests from 2003-2021. Trends were mostly positive, particularly in the northeastern and southern Congo Basin, and were concentrated in areas with high deforestation. Year-to-year variation of fires was synchronized with increasing temperature and vapor pressure deficit. There was anomalously high fire activity across the region during the 2015-2016 El Niño. These results contrast sharply with the drier African woodlands and savannas, where fires have been steadily decreasing. Further attention to fires in African tropical forests is needed to understand their global impacts on carbon storage and their local implications for biodiversity and human livelihoods.

Vector-borne diseases cause significant financial and human loss, with billions of dollars spent on control. Arthropod vectors experience a complex suite of environmental factors that affect fitness, population growth, and species interactions across multiple spatial and temporal scales. Temperature and water availability are two of the most important abiotic variables influencing their distributions and abundances. While extensive research on temperature exists, the influence of humidity on vector and pathogen parameters affecting disease dynamics are less understood. Humidity is often underemphasized, and when considered, is often treated as independent of temperature even though desiccation likely contributes to declines in trait performance at warmer temperatures. This Perspectives explores how humidity shapes the thermal performance of mosquito-borne pathogen transmission. We summarize what is known about its effects and propose a conceptual model for how temperature and humidity interact to shape the range of temperatures across which mosquitoes persist and achieve high transmission potential. We discuss how failing to account for these interactions hinders efforts to forecast transmission dynamics and respond to epidemics of mosquito-borne infections. We outline future research areas that will ground the effects of humidity on the thermal biology of pathogen transmission in a theoretical and empirical framework to improve spatial and temporal prediction of vector-borne pathogen transmission.

Developing and implementing a malaria early warning system that integrates public health surveillance with monitoring of related environmental factors is the goal of the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) project. Collaborating with our Ethiopian partners on requirements, we developed the R package epidemiar to provide a generalized set of functions for disease forecasting, plus customized code including a Google Earth Engine script for environmental data and formatting scripts for distributable reports with maps and graphs. Since 2019, a local team at Bahir Dar University in Ethiopia has been using EPIDEMIA to produce weekly malaria forecasting reports. Intensive anti-malarial efforts in the Amhara region of Ethiopia have resulted in declining malaria incidence, with a 75% decrease in cases between 2013 and 2018 (561,101 to 137,445 cases). In this context of potentially changing malaria transmission patterns, continual model evaluation past the initial model development is warranted. We built model validation and assessment tools into the epidemiar R package for on-demand evaluation for any historical period. Validation statistics included Mean Error (ME), Mean Absolute Error (MAE), and proportion of observations that fell inside the prediction intervals. Evaluation can be made for one through n-week ahead predictions, and include comparisons with two naïve models: persistence of last known value, and average cases from that week of the year. Building validation into the early warning system provides more opportunities to learn about the model via the validation results. We can identify locations where the models perform best with district-level results. With on-demand implementation and time-range flexibility, we can also investigate how accuracy changes over time, which is of particular interest in places like Ethiopia with changing patterns and declining trends due to anti-malarial programs.