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The effect of machine learning and other enhancements on statistical–dynamical forecasts of soil moisture (0–10 and 0–100 cm) and a reference evapotranspiration fraction [evaporative stress index (ESI)] on subseasonal time scales (15–28 days) are explored. The predictors include the current and past land surface conditions and dynamical model hindcasts from the Subseasonal to Seasonal Prediction project (S2S). When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored. Significance Statement: Rapidly intensifying droughts pose extra challenges for predictability. Here, dynamical forecast model output is combined with nonlinear machine learning methods to improve forecasts of rapid changes in soil moisture and the evaporative stress index (ESI). [ABSTRACT FROM AUTHOR]

Background: Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments. Methods: The Planetary Child Health & Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants. Discussion: As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making and disseminating rigorously obtained, generalizable disease burden estimates. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available for download to the research and stakeholder communities. These can then be used as inputs to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection. Study registration: PROSPERO protocol #CRD42023384709. [ABSTRACT FROM AUTHOR]

In 2023 human populations experienced multiple record‐breaking climate events, with widespread impacts on human health and well‐being. These events include extreme heat domes, drought, severe storms, flooding, and wildfires. Due to inherent lags in the climate system, we can expect such extremes to continue for multiple decades after reaching net zero carbon emissions. Unfortunately, despite these significant current and future impacts, funding for research in climate and health has lagged behind that for other geoscience and biomedical research. While some initial efforts from funding agencies are evident, there is still a significant need to increase the resources available for multidisciplinary research in the face of this issue. As a group of experts at this important intersection, we call for a more concerted effort to encourage interdisciplinary and policy‐relevant investigations into the detrimental health effects of continued climate change. Plain Language Summary: Recent climate events highlight the need for additional funding for interdisciplinary research focused on the impact of climate on human health. Key Points: Climate change is increasingly impacting populations across the globeFunding for interdisciplinary research in climate and health has lagged behind biomedical researchWe call for a more proactive effort to encourage interdisciplinary research into the negative effects of climate change [ABSTRACT FROM AUTHOR]

For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden-Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describingMJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a directMJO influence on precipitation corresponding to propagation of the MJO's convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October-November) and southwest monsoon (SWM; May-September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method. [ABSTRACT FROM AUTHOR]

An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics. [ABSTRACT FROM AUTHOR]

Remotely sensed inundation may help to rapidly identify areas in need of aid during and following floods. Here we evaluate the utility of daily remotely sensed flood inundation measures and estimate their congruence with self‐reported home flooding and health outcomes collected via the Texas Flood Registry (TFR) following Hurricane Harvey. Daily flood inundation for 14 days following the landfall of Hurricane Harvey was acquired from FloodScan. Flood exposure, including number of days flooded and flood depth was assigned to geocoded home addresses of TFR respondents (N = 18,920 from 47 counties). Discordance between remotely‐sensed flooding and self‐reported home flooding was measured. Modified Poisson regression models were implemented to estimate risk ratios (RRs) for adverse health outcomes following flood exposure, controlling for potential individual level confounders. Respondents whose home was in a flooded area based on remotely‐sensed data were more likely to report injury (RR = 1.5, 95% CI: 1.27–1.77), concentration problems (1.36, 95% CI: 1.25–1.49), skin rash (1.31, 95% CI: 1.15–1.48), illness (1.29, 95% CI: 1.17–1.43), headaches (1.09, 95% CI: 1.03–1.16), and runny nose (1.07, 95% CI: 1.03–1.11) compared to respondents whose home was not flooded. Effect sizes were larger when exposure was estimated using respondent‐reported home flooding. Near‐real time remote sensing‐based flood products may help to prioritize areas in need of assistance when on the ground measures are not accessible. Plain Language Summary: Flood maps that are prepared using satellites monitoring the earth surface may help to rapidly identify areas in need of aid during and following flooding events. In this study, we quantified the risk of self‐reported health outcomes that are associated with home flooding measured using either satellite‐derived flood maps or self‐reported exposure to floodwaters during and following Hurricane Harvey. Responses from 18,920 participants in the Texas Flood Registry included home location of the responders, reported adverse health symptoms, reported hospitalization, flooding of home, contact with flood water, and demographic variables. Respondents whose homes were flooded as per the flood maps were more likely to report injury, concentration problems, skin rash, illness, headaches, and runny nose compared to respondents whose homes were not flooded. The estimated strength of association was larger when exposure was estimated using respondent‐reported home flooding. Flood maps prepared from satellite data may help to prioritize areas in need of assistance in real‐time when on the ground measures are not available. Key Points: Remote sensing identified exposure to floods was associated with self‐reported flood related adverse health symptoms, illness, and injurySelf‐reported illness and injury increased with increase in proximity to the flood watersThe association estimated using self‐reported exposure to flood were stronger than those estimated using remote sensing [ABSTRACT FROM AUTHOR]

Brazil has been severely affected by the COVID‐19 pandemic. Temperature and humidity have been purported as drivers of SARS‐CoV‐2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil's 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time‐varying reproductive number (Rt) of SARS‐CoV‐2 were examined using generalized additive models fit to data from the entire 15‐month period and several shorter, 3‐month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and Rt. We found that transmission is strongly influenced by unmeasured sources of between‐state heterogeneity and the near‐recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and increased specific humidity with increased transmission. However, the impacts of meteorology, policy, and mobility on Rt varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS‐CoV‐2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID‐19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS‐CoV‐2 transmission. Plain Language Summary: Environmental factors such as outdoor temperature and humidity can affect the spread of the flu and other respiratory viruses. For this reason, early studies on the COVID‐19 pandemic hypothesized that temperature, humidity, and other environmental factors might create favorable or less favorable conditions to facilitate the spread of COVID‐19. At times, politicians and the media have disseminated these hypotheses without proper vetting. COVID‐19 has caused major impacts in Brazil, and in this study we use a statistical model that allows us to investigate how environmental factors, governmental policies, and human mobility are related to COVID‐19 transmission in Brazil from June 2020 to August 2021. We found that temperature and humidity were not very important in explaining COVID‐19 transmission. Governmental policies and human mobility played a larger role in explaining transmission, but whether changes in policies or human mobility led to increased versus decreased transmission varied throughout our study period. These changes with time may explain why the conclusions of other studies on what drives the spread of COVID‐19 may appear at odds with each other. Continuing to rely on proven mitigation measures such as mass vaccinations should be the key priority in the fight against COVID‐19 in Brazil. Key Points: Unmeasured sources of between‐state heterogeneity and recent waves of cases are the dominant drivers of SARS‐CoV‐2 transmission in BrazilThe impacts of policy, meteorology, and mobility on transmission vary in direction and magnitude within subperiods of our studyRelying on proven mitigation measures such as mass vaccinations should be the key priority in the continued fight against COVID‐19 [ABSTRACT FROM AUTHOR]

Migration is an important risk factor for malaria transmission for malaria transmission, creating networks that connect Plasmodium between communities. This study aims to understand the timing of why people in the Peruvian Amazon migrated and how characteristics of these migrants are associated with malaria risk. A cohort of 2,202 participants was followed for three years (July 2006 - October 2009), with thrice-weekly active surveillance to record infection and recent travel, which included travel destination(s) and duration away. Migration occurred more frequently in the dry season, but the 7-day rolling mean (7DRM) streamflow was positively correlated with migration events (OR 1.25 (95% CI: 1.138, 1.368)). High-frequency and low-frequency migrant populations reported 9.7 (IRR 7.59 (95% CI:.381, 13.160)) and 4.1 (IRR 2.89 (95% CI: 1.636, 5.099)) times more P. vivax cases than those considered non-migrants and 30.7 (IRR 32.42 (95% CI: 7.977, 131.765)) and 7.4 (IRR 7.44 (95% CI: 1.783, 31.066)) times more P. falciparum cases, respectively. High-frequency migrants employed in manual labour within their community were at 2.45 (95% CI: 1.113, 5.416) times higher risk than non-employed low-frequency migrants. This study confirms the importance of migration for malaria risk as well as factors increasing risk among the migratory community, including, sex, occupation, and educational status. [ABSTRACT FROM AUTHOR]