Global climate change is redefining our understanding of how diseases spread. In Sri Lanka, vector-borne diseases such as dengue fever historically surged during the monsoon seasons when temperatures were high enough for mosquito eggs to hatch. Unfortunately, due to rising temperatures and more erratic rainfall patterns, mosquito eggs can now hatch year-round making outbreaks increasingly unpredictable, leading to an alarming rise in hospitalizations and deaths. More data is needed to adapt our response to these diseases in an increasingly warmer world. In the contemporary landscape, a wealth of disease information is available, yet accessibility remains limited due to unstructured data formats such as PDFs. Therefore, converting unstructured disease reports into structured formats is necessary for effectively leveraging data. This paper introduces a comprehensive framework for collecting unstructured disease reports and transforming them into analyzable formats. By creating separate models tailored to each data format, we can ensure accuracy compared to general models. These straightforward models enhance accessibility and empower other researchers to use our tools. The returned structured data can then be harnessed for analysis, statistical purposes, and informing evidence-based public health interventions, thus facilitating more informed decision-making in healthcare. We deploy this framework to produce geospatial data for Sri Lanka and Brazil for many different conditions and align these data with satellite environmental data, providing for the first time a structured, aligned powerful dataset for disease modeling.

The global expansion of the dengue belt, driven by climate change and increased urbanization, has led to a significant rise in dengue cases worldwide (1). Early warning systems (EWS) coupled with prompt public health response mechanisms are crucial in mitigating dengue-related morbidity and mortality globally. In Sri Lanka, dengue transmission occurs year-round with two peaks correlating to the southwest monsoon from May to September and the northeast monsoon from October to January (2). The presence of multiple dengue virus serotypes (DENV1–4) complicates epidemiological patterns, as sequential infections with different serotypes can increase the risk of severe disease manifestations detected by surveillance systems (3). Understanding and integrating these virological dynamics, vector dynamics, and real-time surveillance data are essential for developing effective EWS and targeted public health interventions. We propose the use of Graph Neural Networks (GNNs) as an EWS. Using Earth observational data from NASA’s global satellites and dengue incidence data from Sri Lanka’s Ministry of Health, we developed traditional and graph-based EWS to forecast dengue cases across Sri Lanka’s 25 districts between 2013 and 2022. We demonstrate empirically that GNNs incorporating spatiotemporal relations significantly outperform traditional EWS models such as Autoregressive Integrated Moving Average (ARIMA), Random Forest, and Long Short-Term Memory (LSTM). Our source code is available on GitHub.