Micro-climate to macro-risk
Mapping fine scale differences in mosquito-borne disease risk using remote sensing
Bibliographic Data
| ID | 15548121 |
|---|---|
| Authors | Anna Boser (0000-0002-7336-0117, University of California, Santa Barbara, corresponding author), Daniel Sousa (0009-0000-5631-7398), Dan Sousa (0000-0002-1632-1955, San Diego State University), Ashley Larsen (0000-0002-7938-331X, University of California, Santa Barbara), Andrew Macdonald (0000-0003-0172-7749, University of California, Santa Barbara) |
| Year | 2021 |
| Volume | 16 |
| Issue | 12 |
| Pages | 124014-124014 |
| Publication date | 2021-11-02 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ac3589 |
| OpenAlex | W3209946698 |
| Language | EN |
| Citations received | 3 |
| References cited | 47 |
Mosquito-borne diseases (MBD) threaten over 80% of the world’s population, and are increasing in intensity and shifting in geographical range with land use and climate change. Mitigation hinges on understanding disease-specific risk profiles, but current risk maps are severely limited in spatial resolution. One important determinant of MBD risk is temperature, and though the relationships between temperature and risk have been extensively studied, maps are often created using sparse data that fail to capture microclimatic conditions. Here, we leverage high resolution land surface temperature (LST) measurements, in conjunction with established relationships between air temperature and MBD risk factors like mosquito biting rate and transmission probability, to produce fine resolution (70 m) maps of MBD risk components. We focus our case study on West Nile virus (WNV) in the San Joaquin Valley of California, where temperatures vary widely across the day and the diverse agricultural/urban landscape. We first use field measurements to establish a relationship between LST and air temperature, and apply it to Ecosystem Spaceborne Thermal Radiometer Experiment data (2018–2020) in peak WNV transmission months (June–September). We then use the previously derived equations to estimate spatially explicit mosquito biting and WNV transmission rates. We use these maps to uncover significant differences in risk across land cover types, and identify the times of day which contribute to high risk for different land covers. Additionally, we evaluate the value of high resolution spatial and temporal data in avoiding biased risk estimates due to Jensen’s inequality, and find that using aggregate data leads to significant biases of up to 40.5% in the possible range of risk values. Through this analysis, we show that the synergy between novel remote sensing technology and fundamental principles of disease ecology can unlock new insights into the spatio-temporal dynamics of MBDs
Biology · Climate change · Environmental health · Generalized additive model · Geography · Land cover · Land use · Physical geography · Population · Spatial heterogeneity · Statistics · Climate Change and Health Impacts · Environmental Science · Mathematics · Mosquito-borne diseases and control · Viral Infections and Vectors · Ecology
| Citations per year | 3 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |