Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global "One Health" initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014-2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014-2018). Consistent with suggestions that KFD is an "ecotonal" disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high PLOS NEGLECTED TROPICAL DISEASES

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The overall precipitation in the state of Bihar, India is showing a decreasing trend both annually and seasonally, and yet extreme flood events are on the rise. The Kosi river embankments built to safeguard communities against flood risk are a product of socio-political and historical events in the past, but have resulted in differential impacts on those living inside and outside these embankments. The geomorphology of the river Kosi also makes it highly susceptible to recurring floods because it forms one of the largest inland deltas in North Bihar. Flood protection structures such as embankments exacerbate the magnitude of floods by jacketing the heavy sediment load and thus raising the riverbeds and exacerbating the intensity and duration of floods. Our paper employs an interdisciplinary approach to analysing both the biophysical and socio-institutional causalities of increasing flood events. From the quantitative analysis of rainfall data, we find that the daily, as well as monthly rainfall alone are not responsible for extreme flood events. The extreme rainfall events in the summer monsoon also do not increase the odds of flooding. Therefore, we conclude that precipitation alone is not the main factor affecting community's vulnerabilities but, a combination of socio-institutional factors including spatial location with respect to the embankment, class and caste of these communities. Our statistical analysis correlating daily and monthly gridded rainfall to the occurrences of flooding at the district level suggest that there are fewer flood events in the presence of the embankments across all years. However, primary data from household interviews and field observations confirm that the frequency and intensity of floods have increased in the post-embankment period. We found that the breaching of the river embankments is one of the major factors responsible for floods outside of the embankments. Kosi's marginalized communities perceive that they have become more vulnerable to flood risk in the post-embankment period with a declining standard of living in the Kosi villages caused by lack of proper roads, economic opportunities, educational institutions, public utilities and healthcare facilities, especially in areas with embankments.

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