Pranav S. Pandit

The emergence of Highly Pathogenic Avian Influenza (HPAI 2.3.4.4b) in dairy herds in 2024 across 19 states in the United States of America has raised concerns regarding the potential national and global zoonotic impact. All recent modeling efforts implemented homogenous cattle-to-cattle (both intra and inter-herd) transmission models which did not capture the real-world heterogeneity in mixing of animals, individual variations in susceptibility and infectiousness and clinical incidences across pens and lactation groups. The aim of this study was to develop a heterogenous transmission model to estimate the epidemiological parameters for intra-herd HPAI transmission on Californian dairies. We developed a validated stochastic agent-based model to estimate the epidemiological parameters for intra-herd HPAI transmission on California dairies. The hierarchical agent-based model also parameterized stochastic cattle movements within-herd to simulate real dairy management practices. A cow-level SEIR transmission approach was assumed during the outbreak. A novel Bayesian Optimizer with Gaussian Process (BO-GP) was fitted to the agent-based model for validation which converged within 25-40 iterations (out of 100 per farm) with minimal loss over two distinct error metrics, namely, Poisson loss function and temporal distance metric. Our optimized simulations estimated an average R ₀ was around 10.7-10.8 across all farms within the first 15 days of observed outbreak on four dairy farms with a mean effective transmission rate of 4.5% per contact between susceptible and infectious cows within each pen. Our model demonstrated that the movement of cows between pens ensured localized clusters of outbreaks within the sub-herds (pen population) that prolonged the overall outbreak within farms. We estimated the total duration of infection between 14.5 and 28 days, which is higher than the estimates from the homogenous models. With an integrated hierarchical agent-based model combined with Bayesian approximation, we produced actionable insights on the epidemiology of intra-farm spread of HPAI within cow herds, thereby guiding both future model development and applied disease control strategy.

Anthropogenic climate change is reshuffling global biodiversity and accelerating zoonotic spillover at the human-wildlife interface. The 2026 multi-country cruise ship outbreak of Andes virus highlights the unpredictable and globalized threat posed by rodent-borne orthohantaviruses, which cause severe, highly fatal human diseases worldwide. Despite this significant public health risk, macroecological synthesis linking environmental drivers to systemic shifts in hantavirus hazards has been lacking. Here, we implement an integrated ecological and machine learning framework combining 89 unique virus-host associations across 61 reservoir species with mechanistic Force-of-Infection (FOI) modeling to project global spillover hazards under current and future climate scenarios (SSP2-4.5 and SSP5-8.5) for 2041–2060. Our models demonstrate a spatially uneven but geographically extensive expansion of spillover hazards, with approximately 74.9% of modeled land area experiencing an increase in FOI. We identify three distinct high-hazard belts heavily concentrated in already-temperate reservoir ranges: western and central North America, central and northeastern Europe extending into western Russia, and discrete patches within central South America. Crucially, our findings reveal that hantavirus hazards are fundamentally shaped along virus-specific ecological axes. While thermal and precipitation anomalies predominate the transmission dynamics of Old and New World lineages like Sin-Nombre virus and Thailand virus, distinct urban-centric Profiles emerge for Seoul virus and Mamore virus, driven explicitly by fine-scale anthropogenic landscape modifications. These results reveal that climate change will not simply intensify viral spillover uniformly but will fundamentally restructure it across varied climatic and human-dominated environments. Our maps provide a critical baseline for targeted global surveillance and highlight the urgent need to integrate spatial conservation planning with public health preparedness.

Climate change is expected to significantly alter the ecological dynamics of zoonotic diseases, yet its long-term impact on rodent-borne hemorrhagic fevers in South America remains poorly understood. Here, we developed a robust predictive modeling framework that integrates species distribution models with a mechanistic force-of-infection approach to evaluate the effects of climate change on zoonotic risk of New World Arenaviruses. Using climate projections under Shared Socioeconomic Pathways SSP2-4.5 (moderate) and SSP5-8.5 (severe), our models predict a substantial increase in spillover risk across endemic and non-endemic regions over the next two decades. Projected increases in spillover risk for Guanarito, Machupo, and Junin viruses are primarily driven by climate-induced shifts in temperature seasonality, reduced precipitation, and expanding anthropogenic land use, particularly cropland and urban areas within reservoir habitats. Our projections identify transboundary arenaviral hotspots, underscoring the urgent need for coordinated climate-adaptive public health policies, including cross-border surveillance efforts, land-use planning, and resilient rural health systems.

Emerging infections and environmental disruptions increasingly threaten wildlife and ecosystem health. Free-ranging wildlife often serve as early indicators of ecological instability, making timely detection of morbidity and mortality events critical for early warning. Yet, existing systems lack the analytical capacity for real-time outbreak detection. We present WildAlert, an AI-driven early warning system that integrates fine-tuned BERT-based natural language processing models with unsupervised anomaly detection framework to identify unusual wildlife health events using real-time pre-diagnostic clinical data from wildlife rehabilitation organizations. The NLP module achieved high accuracy across clinical classifications and circumstances of admissions, enabling a pre-diagnostic syndromic surveillance framework. Retrospective validation demonstrated that WildAlert anomalies frequently coincided with or preceded confirmed morbidity events, including highly pathogenic avian influenza (HPAI), harmful algal bloom–associated toxicosis, cold-stunning in sea turtles, mass stranding events, West Nile virus, and mycoplasmosis. WildAlert establishes the world’s largest standardized, near real-time wildlife health surveillance system, transforming wildlife rehabilitation clinical records into actionable intelligence capable of detecting anomalies across taxa and regions, often before other surveillance methods. WildAlert provides a transferable analytical framework and scalable One Health model linking biodiversity monitoring, zoonotic disease preparedness, and ecosystem-linked environmental threats, with implications for conservation, public health and environmental hazard response.

The drylands vesper mouse (Calomys musculinus) is the primary host for Junin mammarenavirus (JUNV), the etiological agent of Argentine hemorrhagic fever in humans. We assessed the potential distribution of C. musculinus and identified disease transmission hotspots under current climatic conditions and projected future scenarios, including severe (Representative Concentration Pathway 8.5) and intermediate (Representative Concentration Pathway 4.5) climate change scenarios in 2050 and 2070. Utilizing tree-based machine learning algorithms, we modeled C. musculinus distribution by incorporating bioclimatic and landscape predictors. The model showed strong performance, achieving F-scores between 80.22 and 83.09%. Key predictors indicated that C. musculinus prefers warm temperatures, moderate annual precipitation, low precipitation variability, and low pasture coverage. Under the severe climate change scenario, suitable areas for the rodent and hotspots for potential disease decreased. The intermediate scenario showed an expansion in C. musculinus distribution alongside increased potential hotspot zones. Despite the complexity of ecological systems and the limitations of the model, our findings offer a framework for preventive measures and ecological studies in regions prone to the expansion of C. musculinus and in hotspots for disease transmission driven by climate change.

Background: The drylands vesper mouse ( Calomys musculinus) is the principal host for Junin mammarenavirus (JUNV), which causes Argentine Hemorrhagic Fever (AHF) in humans. In our study, we aimed to assess the probable range of C. musculinus and to identify hotspots for potential disease transmission to humans under current and future climate change scenarios. Methodology/Principal Findings: We used tree-based machine learning (ML) classification algorithms to generate and project C. musculinus habitat suitability under two climate change scenarios for the years 2050 and 2070 using bioclimatic and landscape related predictors. Evaluation of the models showed high accuracy, with AUC _ROC ranging from 86.81-89.84%. The analysis of the importance and influence of different variables indicated that the rodent prefers warm temperatures, moderate annual precipitation, low precipitation variability, and low pasture coverage. While a severe climate change scenario (Representative Concentration Pathway 8.5) suggests a reduction in suitable areas for JUNV reservoir and a decrease in hotspots for potential disease transmission, an intermediate scenario (Representative Concentration Pathway 4.5) displays expansion in areas for C. musculinus distribution alongside increased potential hotspot zones. Conclusions/Significance: While acknowledging the complexity of ecological systems and the limitations of the species distribution models, our findings offer a framework for developing preventive measures and conducting ecological studies in regions prone to C. musculinus expansion and hotspots for potential disease transmission driven by climate change. Preventive interventions will need to be adapted to target C. musculinus changing spatial dynamics. Author Summary: Climate change might modify where animals live, including those that carry diseases than can spread to humans. This study focused on how climate change might affect the drylands vesper mouse ( Calomys musculinus ). This rodent carries the Junin virus, which causes Argentine Hemorrhagic Fever, a dangerous disease if left untreated. We used species distribution models to predict how this rodent’s range might change by 2050 and 2070 under an intermediate and severe climate change scenario. We found that a temperature rise of 1.1°C to 2.6°C (intermediate change scenario) could allow the rodent to move into new areas, potentially increasing disease transmission to humans. These new areas might have warm temperatures, moderate rainfall, and low grass cover, as we found that C. musculinus prefers those climate and landscape conditions. Knowing the rodent’s potential distribution and preferred habitat enables preparedness and monitoring for early warning systems. By establishing monitor systems and online platforms featuring species distribution models and predictions, we can facilitate communication and resource sharing among researchers, policymakers, healthcare professionals and the public. These tools will help protect public health as climate change continues to bring new challenges.

Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the “wisdom of crowds” public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820–22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor—the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1–2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.

Background: Rapid and accurate markers to aid diagnosis of sepsis are needed in neonatal foals. The CBC variable red blood cell distribution width (RDW) to platelet ratio (RPR) is associated with inflammatory response and linked to poor outcomes of sepsis in human patients. Hypothesis: Explore the correlation of RPR with sepsis in neonatal foals and evaluate RPR predictive and prognostic value. Animals: Three hundred seventeen hospitalized neonatal foals ≤7 days of age that had a CBC and physical exam performed at admission between 2012 and 2021. Methods: Retrospective case-control study. Clinical records were used to calculate sepsis scores and define groups. Red blood cell distribution width to platelet ratio was calculated and compared between groups (septic vs nonseptic) based on Kruskal-Wallis and Wilcoxon signed-rank tests. A multivariate logistic regression model to predict sepsis was created. The cutoff for RPR was obtained based on the maximal Youden Index. The Kaplan-Meier method and the log-rank test were used to estimate survival curves and compare survival rates based on RPR. Results: Red blood cell distribution width to platelet ratio was significantly higher in septic foals (Median = 0.099, confidence interval [CI] [0.093; 0.108]) than in sick nonseptic (0.085, CI [0.083; 0.089]) and healthy foals (0.081, CI [0.077; 0.086]; P < .0001). Red blood cell distribution width to platelet ratio was able to predict sepsis with high accuracy (AUC = 82.1%). The optimal RPR cutoff for sepsis was 0.09. Conclusions and Clinical Importance: Red blood cell distribution width to platelet ratio calculation is practical, inexpensive, and based on CBC-derived data. Calculation of RPR along with CBC can aid in the diagnosis of sepsis and estimation of outcome.