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Background Dengue fever is the most common arboviral disease in humans, with an estimated 50-100 million annual infections worldwide. Dengue fever cases have increased substantially in the past four decades, driven largely by anthropogenic factors including climate change. More than half the population of Peru is at risk of dengue infection and due to its geography, Peru is also particularly sensitive to the effects of El Niño Southern Oscillation (ENSO). Determining the effect of ENSO on the risk for dengue outbreaks is of particular public health relevance and may also be applicable to other Aedes-vectored viruses. Methods We conducted a time-series analysis at the level of the district-month, using surveillance data collected from January 2000 to September 2018 from all districts with a mean elevation suitable to survival of the mosquito vector (<2,500m), and ENSO and weather data from publicly-available datasets maintained by national and international agencies. We took a Bayesian hierarchical modeling approach to address correlation in space, and B-splines with four knots per year to address correlation in time. We furthermore conducted subgroup analyses by season and natural region. Results We detected a positive and significant effect of temperature (°C, RR 1.14, 95% CI 1.13, 1.15, adjusted for precipitation) and ENSO (ICEN index: RR 1.17, 95% CI 1.15, 1.20; ONI index: RR 1.04, 95% CI 1.02, 1.07) on outbreak risk, but no evidence of a strong effect for precipitation after adjustment for temperature. Both natural region and season were found to be significant effect modifiers of the ENSO-dengue effect, with the effect of ENSO being stronger in the summer and the Selva Alta and Costa regions, compared with winter and Selva Baja and Sierra regions. Conclusions Our results provide strong evidence that temperature and ENSO have significant effects on dengue outbreaks in Peru, however these results interact with region and season, and are stronger for local ENSO impacts than remote ENSO impacts. These findings support optimization of a dengue early warning system based on local weather and climate monitoring, including where and when to deploy such a system and parameterization of ENSO events, and provide high-precision effect estimates for future climate and dengue modeling efforts.

SARS-CoV-2 likely emerged from an animal reservoir. However, the frequency of and risk factors for interspecies transmission remain unclear. We conducted a community-based study in Idaho, USA, of pets in households that had >1 confirmed SARS-CoV-2 infections in humans. Among 119 dogs and 57 cats, clinical signs consistent with SARS-CoV-2 were reported for 20 dogs (21%) and 19 cats (39%). Of 81 dogs and 32 cats sampled, 40% of dogs and 43% of cats were seropositive, and 5% of dogs and 8% of cats were PCR positive. This discordance might be caused by delays in sampling. Respondents commonly reported close human‒animal contact and willingness to take measures to prevent transmission to their pets. Reported preventive measures showed a slightly protective but nonsignificant trend for both illness and seropositivity in pets. Sharing of beds and bowls had slight harmful effects, reaching statistical significance for sharing bowls and seropositivity.

More than one billion people rely on livestock for income, nutrition, and social cohesion, however livestock keeping can facilitate disease transmission and contribute to climate change. While data on the distribution of livestock have broad utility across a range of applications, efforts to map the distribution of livestock on a large scale are limited to the Gridded Livestock of the World (GLW) project. We present a complimentary effort to map the distribution of cattle and pigs in Malawi, Uganda, Democratic Republic of Congo, and South Sudan. In contrast to GLW, which uses dasymmetric modeling applied to census data to produce time-stratified estimates of livestock counts and spatial density, our work uses complex survey data and distinct modeling methods to generate a time-series of livestock distribution, defining livestock density as the ratio of animals to humans. In addition to favorable cross-validation results and general agreement with national density estimates derived from external data on national human and livestock populations, our results demonstrate extremely good agreement with GLW-3 estimates, supporting the validity of both efforts. Our results furthermore offer a high-resolution time series result and employ a definition of density which is particularly well-suited to the study of livestock-origin zoonoses.

Introduction/Objective: This study evaluates 4 years of data from the Seattle One Health Clinic (OHC), a novel model for clinical care which integrates human and animal health care services for youth and young adults experiencing homelessness (YPEH) and their pets. Methods: We analyzed deidentified data from standardized OHC visit forms, electronic medical records, and veterinary records from 2019 to 2022. We assessed the overlaps between human and animal healthcare provided and the impact of environmental stressors on both human and animal patients. Results: Over 50% of all human clients established healthcare for the first time in 2 years, with 85% attending one or more follow-up appointments with non-emergency healthcare services within 2 years following their initial OHC appointment. All animals received care during their visit. Needs addressed at the human-animal interface included zoonotic infections, animal allergies, and mental/behavioral health. The most common client-pet reported environmental concerns were food insecurity, heat, and cold. Conclusion: Our results suggest integrated human and animal healthcare is a feasible and acceptable model of care for YPEH to access acute and preventative care at the human-animal-environmental interface. This approach holds promise for increasing health-seeking behaviors, and engagement in preventative, therapeutic, and follow-up care.

Pediatric diarrhea, a leading cause of under-five mortality, is predominantly infectious in etiology. As many putative causal agents are zoonotic, animal exposure is a likely risk factor. To evaluate the effect of animal-related factors on moderate to severe childhood diarrhea in rural Kenya, where animal contact is common, Conan et al. studied 73 matched case-control pairs from 2009-2011, collecting rich exposure data on many dimensions of animal contact. We review the challenges associated with analyzing moderately-sized datasets with a large number of predictors and present two alternative methodological approaches. We conducted a simulation study to demonstrate that forward stepwise selection results in overfit models when data are high-dimensional, and that p values reported directly from the data used to train these models are misleading. We described how automated methods of variable selection, attractive when the number of predictors is large, can result in overadjustment bias. We proposed an alternative a priori regression approach not subject to this bias. Applied to Conan et al.'s data, this approach found a non-significant but positive trend for household's sharing of water sources with livestock or poultry, child's presence for poultry slaughter, and child's habit of playing where poultry sleep or defecate. For many predictors evaluated few pairs were discordant, suggesting matching compromised the power of this analysis. Finally, we proposed latent variable modeling as a complimentary approach and performed Item Response Theory modeling on Conan et al.'s data, with animal contact as the latent trait. We found a moderate but non-significant effect (OR 1.21, 95% CI 0.78, 1.87, unit = 1 standard deviation). Automated methods of model selection are appropriate for prediction models when fit and evaluated on separate samples. However when the goal is inference, these methods can produce misleading results. Furthermore, case-control matching should be done with caution.

Domestic and wild animals are important reservoirs of the rhodesiense form of human African trypanosomiasis (rHAT), however quantification of this effect offers utility for deploying non-medical control activities, and anticipating their success when wildlife are excluded. Further, the uncertain role of animal reservoirs—particularly pigs—threatens elimination of transmission (EOT) targets set for the gambiense form (gHAT). Using a new time series of high-resolution cattle and pig density maps, HAT surveillance data collated by the WHO Atlas of HAT, and methods drawn from causal inference and spatial epidemiology, we conducted a retrospective ecological cohort study in Uganda, Malawi, Democratic Republic of the Congo (DRC) and South Sudan to estimate the effect of cattle and pig density on HAT risk. For rHAT, we found a positive effect for cattle (RR 1.61, 95% CI 0.90, 2.99) and pigs (RR 2.07, 95% CI 1.15, 2.75) in Uganda, and a negative effect for cattle (RR 0.88, 95% CI 0.71, 1.10) and pigs (RR 0.42, 95% CI 0.23, 0.67) in Malawi. For gHAT we found a negative effect for cattle in Uganda (RR 0.88, 95% CI 0.50, 1.77) and South Sudan (RR 0.63, 95% CI 0.54, 0.77) but a positive effect in DRC (1.17, 95% CI 1.04, 1.32). For pigs, we found a positive gHAT effect in both Uganda (RR 2.02, 95% CI 0.87, 3.94) and DRC (RR 1.23, 95% CI 1.10, 1.37), and a negative association in South Sudan (RR 0.66, 95% CI 0.50, 0.98). These effects did not reach significance for the cattle-rHAT effect in Uganda or Malawi, or the cattle-gHAT and pig-gHAT effects in Uganda. While ecological bias may drive the findings in South Sudan, estimated E-values and simulation studies suggest unmeasured confounding and underreporting are unlikely to explain our findings in Malawi, Uganda, and DRC. Our results suggest cattle and pigs may be important reservoirs of rHAT in Uganda but not Malawi, and that pigs—and possibly cattle—may be gHAT reservoirs.

Background A proactive approach to preventing and responding to emerging infectious diseases is critical to global health security. We present a three-stage approach to modeling the spatial distribution of outbreak vulnerability to Aedes aegypti -vectored diseases in Perú. Methods Extending a framework developed for modeling hemorrhagic fever vulnerability in Africa, we modeled outbreak vulnerability in three stages: index case potential (stage 1), outbreak receptivity (stage 2), and epidemic potential (stage 3), stratifying scores on season and El Niño events. Subsequently, we evaluated the validity of these scores using dengue surveillance data and spatial models. Results We found high validity for stage 1 and 2 scores, but not stage 3 scores. Vulnerability was highest in Selva Baja and Costa, and in summer and during El Niño events, with index case potential (stage 1) being high in both regions but outbreak receptivity (stage 2) being generally high in Selva Baja only. Conclusions Stage 1 and 2 scores are well-suited to predicting outbreaks of Ae. aegypti -vectored diseases in this setting, however stage 3 scores appear better suited to diseases with direct human-to-human transmission. To prevent outbreaks, measures to detect index cases should be targeted to both Selva Baja and Costa, while Selva Baja should be prioritized for healthcare system strengthening. Successful extension of this framework from hemorrhagic fevers in Africa to an arbovirus in Latin America indicates its broad utility for outbreak and pandemic preparedness and response activities.

While mainstream climate science continues to dismiss Indigenous Knowledges, Indigenous sentinel networks (ISNs) and resistance movements against extractive energy projects [5] demonstrate why Indigenous epistemologies must be central to efforts that focus on mitigating climate change as opposed to forcing communities to adapt. [...]Indigenous communities from the Americas (e.g. Arctic to the Amazon rainforest) have launched ISNs based on their epistemologies. Societal reliance on fossil fuels desecrates Indigenous lands and increases greenhouse gas emissions, despite Indigenous peoples’ opposition to the ongoing construction of extractive energy infrastructure, e.g., oil pipelines [11]. PLOS Clim 1(5): e0000032. https://doi.org/10.1371/journal.pclm.0000032 About the Authors: Jessica Hernandez E-mail: jhernan@uw.edu Affiliations: Division of Physical Sciences, School of Science, Technology, Engineering & Mathematics, University of Washington Bothell, Bothell, Washington, United States of America, International Mayan League, Eaton House C/O International Mayan League, Washington, D.C., United States of America, Center for One Health Research, Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, United States of America ORICD: https://orcid.org/0000-0003-3825-9432 Julianne Meisner Affiliation: Center for One Health Research, Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, United States of America ORICD: https://orcid.org/0000-0001-7477-6598 Lara A. Jacobs Affiliation: Department of Forest Ecosystems and Society, College of Forestry, Oregon State University, Corvallis, OR, United States of America ORICD: https://orcid.org/0000-0002-1996-2272 Peter M. Rabinowitz Affiliation: Center for One Health Research, Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, United States of America ORICD: https://orcid.org/0000-0002-6873-0208 1.

In recent decades, remarkable progress in the control of Human African trypanosomiasis (HAT)---a bloodborne protozoal parasite transmitted by the tsetse fly (Glossina species)---has led the WHO to set targets for elimination as public health problem (EPHP) by 2020, and elimination of transmission (EOT) by 2030. Global EPHP targets were met in 2018, however most endemic countries are not yet eligible for national EPHP validation, and there are significant challenges to achieving EOT goals. Two forms of HAT, which are geographically- and epidemiologically-distinct, exist: the chronic form, caused by Trypanosoma brucei gambiense (gHAT) and endemic in western and central Africa, and the acute form, caused by T. b. rhodesiense (rHAT) and endemic in eastern and southern Africa. Due to the known importance of animal reservoirs for rHAT EOT targets are set for gHAT alone, resulting in significantly lower investment in rHAT surveillance and control compared with gHAT. Combined with rHAT's acute progression this results in significant underreporting, raising concerns that rHAT will emerge as a major public health problem once gHAT EOT is achieved and donor attention moves away from HAT. With regards to gHAT, uncertainty surrounding animal reservoirs---in particular domestic pigs---as well as latent human reservoirs and undercoverage of high-risk groups by active surveillance activities threaten both the probability of EOT, and re-emergence following EOT. In this dissertation, we used data from the WHO Atlas of HAT, spatial epidemiologic methods, methods drawn from the potential outcomes framework of causal inference, and a stochastic compartmental model to estimate the effect of pig density on HAT risk, to evaluate the feasibility of rHAT EOT with control of domestic animal reservoirs alone, and to decompose the livestock density - HAT effect into three components: (1) the reservoir effect, whereby domestic cattle and pigs infected with trypanosomes serve as a source of human infection, (2) the zooprophylactic effect, whereby tsetse fly preference for livestock protects humans from bites---and therefore trypanosome infection---when livestock are nearby, and (3) the environmental change effect, whereby livestock keeping results in environmental changes that in turn modify tsetse distribution and HAT risk. We conducted this work in four high-burden countries which do not meet WHO criteria for EPHP validation: Uganda (gHAT and rHAT), South Sudan (gHAT), Malawi (rHAT), and Democratic Republic of Congo (DRC, gHAT). Our results suggest pigs may indeed be gHAT reservoirs and, if so, EOT will not be achieved without intervention---trypanocide or, preferably, insecticide treatment---on this reservoir. With regards to rHAT, we found control of the domestic cattle and pig reservoir is critical to control of the disease, in particular in Uganda, but does not lead to EOT. We found evidence of a zooprophlyactic effect in Malawi and South Sudan for both cattle and pigs and in gHAT foci in Uganda for cattle, however we did not detect compelling evidence of an environmental change effect. These results point to the utility of a One Health approach to HAT control, and represent an important contribution to the HAT literature and the efforts of National Sleeping Sickness Control Programs in the study countries. In conjunction with the high-resolution livestock density maps we have produced, our findings will support targeted delivery of expanded and/or enhanced HAT control efforts. Delivering these efforts in a One Health framework, whereby control of animal African trypanosomiasis is coordinated with that of HAT, will increase the likelihood and sustainability of gHAT elimination and ensure rHAT does not subsequently emerge as a major public health problem, reducing the burden of this highly-fatal and poverty-reinforcing disease.