Erin Clancey

Zoonotic pathogens pose significant risk to human health, with spillover into human populations contributing to chronic disease and epidemics. Despite the widely recognized burden of zoonotic spillover, our ability to identify which animal populations serve as primary reservoirs remains incomplete. This challenge is compounded when prevalence in reservoir populations reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections or predict when spillover risk is likely to be greatest. Thus, we develop a general mathematical model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens. Using simulated data, we show that our methodology reliably identifies times when pathogen prevalence is expected to peak. Then, we demonstrate an implementation of our method using previously published surveillance data in straw-colored fruit bats (Eidolon helvum). The generality and simplicity of our methodology make it broadly applicable to a wide range of putative reservoir species where seasonal patterns of birth lead to cyclic, but potentially short-lived, pulses of pathogen prevalence.

Rift Valley fever (RVF) is a zoonotic disease that causes sporadic, multi-country epidemics. However, there is limited understanding of RVF virus circulation during interepidemic periods and the potential impacts of global change on interepidemic RVF. To address these knowledge gaps, we built a predictive model using recent interepidemic RVF outbreak data from Kenya, Tanzania and Uganda. We then projected interepidemic RVF risk for three future time periods (2021–2040, 2041–2060, 2061–2080) under three global change scenarios representing different trajectories for climate and human population distribution (SSP126, SSP245, SSP370). Our model identified interepidemic RVF risk hotspots in east Kenya, east Tanzania and southwest Uganda. Hydrology was a major driver of disease risk: hotspots emerged in association with lakes and rivers, and risk peaked during May–July following the long rains season (March–May). Projections under global change scenarios suggested that disease risk will generally decrease over time. Nevertheless, owing to expected human population growth, we estimate that > 90 million people in the study region will be exposed to interepidemic RVF by 2061–2080, which is nearly double the historical (1970–2000) estimate of approximately 49 million people. Mitigating the future health impacts of RVF will require increased disease surveillance, prevention and control effort in risk hotspots.

Environments can shape the occurrence and extent of disease outbreaks in wildlife. We studied the effects of environmental features on the occurrence of treponeme‐associated hoof disease (TAHD), an emerging infectious disease of free‐ranging elk ( Cervus canadensis ), in southwestern Washington, USA. During the 2016–2022 harvest seasons, successful elk hunters returned mandatory harvest reports and noted the presence or absence of hoof abnormalities indicative of TAHD. We used generalized linear models and an information‐theoretic approach to model selection to relate (1) the spatial distribution of hoof abnormalities to features of landscapes (land cover, topography, and soil characteristics) and (2) the temporal distribution of hoof abnormalities to precipitation during the year preceding the harvest season. The probability of hoof disease increased with soil clay content and proportion of agricultural land (88% of model weight). We found no conclusive evidence for an effect of precipitation on the occurrence of TAHD, but this could relate to relatively high annual precipitation (>140 cm) in the study area. Nevertheless, disease cases may have been negatively associated with precipitation during February–June (55% of model weight). Soils and land management practices may increase the risk of hoof disease by promoting the survival of pathogens that cause TAHD, the susceptibility of elk to infection, or the intensity of pathogen transmission among elk when congregated. Focusing on areas where the risk of disease is greatest may facilitate the detection of TAHD during surveillance. Likewise, removing infected elk and dispersing uninfected elk from areas with the greatest risk of disease may enhance the effectiveness of efforts to reduce transmission. Basing this work on the knowledge that disease risk is modified by factors of hosts, pathogens, and environments, this study serves as an application of the epidemiological triad framework to better understand the ecology and epidemiology of an emerging infectious disease in wildlife.

Institutions of higher education faced a number of challenges during the COVID-19 pandemic. Chief among them was whether or not to reopen during the second wave of COVID-19 in the fall of 2020, which was controversial because incidence in young adults was on the rise. The migration of students back to campuses worried many that transmission within student populations would spread into surrounding communities. In light of this, many colleges and universities implemented mitigation strategies, with varied degrees of success. Washington State University, located in the city of Pullman in Whitman County, WA, is an example of this type of university-community co-location, where the role of students returning to the area for the fall 2020 semester was contentious. Using COVID-19 incidence in Pullman, WA, reported to the Whitman County Health Department, we retrospectively study the transmission dynamics that occurred between the student and community subpopulations in fall 2020. We develop a two-population ordinary differential equations mechanistic model to infer transmission rates within and across the university student and community subpopulations. We use results from Bayesian parameter estimation to determine if exponential transmission of COVID-19 occurred in Pullman, WA, and the magnitude of cross-transmission from students to community members. We find these results are consistent with the estimation of the time-varying reproductive number that outbreak potential was minimal and resolved quickly, and conclude that the students returning to Washington State University-Pullman did not place the surrounding community at disproportionate risk of COVID-19 during fall 2020 when mitigation efforts were in place.

Background: Recent Rift Valley fever (RVF) epidemiology in the eastern Africa region is characterized by widening geographic range and increasing frequency of small disease clusters. Here we conducted studies in the southwestern (SW) Uganda region that has since 2016 reported increasing RVF activities. Methods: A 22-month long hospital-based study in 3 districts of SW Uganda targeting patients with acute febrile illness or unexplained bleeding was followed by a cross-sectional population-based human-animal survey. We then estimated RVF virus (RVFV) force of infection and yearly cases using age-structured seroprevalence data and conducted genomic phylodynamic modelling of RVFV isolates. Results: Overall RVF prevalence was 10.5% (205/1968) among febrile or hemorrhagic cases, including 5% (100/1968) with acute (PCR or IgM positive) infection, averaging 5 cases per month. Community-based seroprevalence of 11.8% (88/743) among humans and 14.6% (347/2383) in livestock was observed. Expected yearly human RVF cases were 314–2111 per 1369 km2 in SW Uganda, up to 3-fold higher than the 0–711 yearly cases in comparable regions of Kenya and Tanzania. Viral genomic studies identified RVFV lineage C, subclade C.2.2 as the circulating strain in SW Uganda since 2019. Lineage C strain has undergone recent rapid evolution and clonal expansion resulting in 4 subclades, C.1.1, C.1.2, C.2.1, and C.2.2, that are adept at establishing endemicity in new territories. Conclusions: We demonstrate an atypical RVF hyperendemic region in SW Uganda characterized by sustained human clinical RVF cases, unusually high population prevalence, and high number of expected yearly human cases, associated in part with emergence of new RVFV sublineages.

Background Rift Valley fever (RVF) is a zoonotic disease that causes sporadic, multi-country epidemics. However, RVF virus (RVFV) also circulates during inter-epidemic periods. There is limited understanding of how climate change will affect inter-epidemic RVF. Here, we project inter-epidemic RVF risk under future climate scenarios, focusing on the East African countries of Kenya, Tanzania, and Uganda. Methods We combined data on inter-epidemic RVF outbreaks and spatially-explicit predictor variables to build a predictive model of inter-epidemic RVF risk. We validated our model using RVFV serological data from humans. We then projected inter-epidemic RVF risk for three future time periods (2021-2040, 2041-2060, 2061-208) under three climate scenarios (SSP126, SSP245, SSP370). Finally, we combined risk projections with human population projections to estimate the future population at risk of inter-epidemic RVF across the study region. Findings Our model showed seasonality in inter-epidemic RVF, with risk peaking May-July following the long rains (March-May). Projections for future climate scenarios suggested that disease risk will increase January-March, with the present-day hotspots of east Kenya, southeast Tanzania, and southwest Uganda remaining high-risk. By 2061-2080, > 117 million people in the study region may be at risk from inter-epidemic RVF, a fourfold increase relative to the historical (1970-2000) estimate of ∼25 million people. Interpretation Climate change will shift the inter-epidemic RVF risk landscape, with increasing short rains (October-December) driving increased risk January-March. Mitigating the future health impacts of RVF will require increased disease surveillance, prevention, and control effort in risk hotspots. Funding US National Institutes of Health. Evidence before this study As for arboviruses generally, global climate change may shift the geographic distribution, timing, and severity of disease burden imposed by Rift Valley fever (RVF). To review the existing evidence for climate change impacts on RVF while adopting a specific geographic focus on East Africa, we searched Web of Science using the query string “ALL=(climate change) AND ALL=(Rift Valley fever) AND ALL=(‘East Africa’ OR Kenya OR Tanzania OR Uganda)”. Our search returned 39 results published between 2007 and 2024. We reviewed these contents to identify work with a substantive focus on understanding how future climate change will affect RVF (as opposed to investigations discussing any connection between climatic factors and RVF occurrence). We identified a total of ten papers that met this criterion: two were reviews, four analyzed mechanistic compartmental models, and four were empirical papers adopting other modeling and analysis methods. These studies highlighted the key role of precipitation and temperature in RVF epidemiology. Heavy precipitation is a well- known RVF driver given that rainfall can cause large amounts of surface water to become available for mosquito vector breeding. As such, expected wetting trends in East Africa under climate change scenarios could fuel increased frequency and severity of RVF. Temperature influences on RVF are underappreciated relative to precipitation, but climate-driven compartmental models emphasize that RVFV transmission is likely to be highest in areas that maintain optimal temperatures for mosquito development (∼22-26°C). Added value of this study Adopting a machine learning approach, we found that precipitation, goat density, soil silt, and elevation were among the most important predictors of inter-epidemic RVF. Our model, which was trained on monthly climatic data, showed seasonally-varying RVF risk, with risk peaking following the long rains season (March-May) and, to a lesser degree, following the short rains (October-December). We used our trained model, which was validated against serological data from humans, to project RVF risk for three future time periods (2021- 2040, 2041-2060, 2061-208) under three climate scenarios (SSP126, SSP245, SSP370) using 11 climate models from the Coupled Model Intercomparison Project (CMIP6). As a result, our multi-model projections of inter-epidemic RVF risk explicitly incorporate multiple sources of climate uncertainty. Projections suggested that RVF risk will increase January-March across the study region, particularly under high-emissions scenarios (i.e., SSP370). Although there are some anticipated changes in the spatial distribution of RVF risk, future risk hotspots largely mirror the present-day, with high risk in east Kenya, southeast Tanzania, and southwest Uganda. Implications of all the available evidence Precipitation, the major driver of RVF, shapes both the temporal and spatial patterning of RVF risk across East Africa. Projections of future RVF risk are also strongly influenced by precipitation, with projected increases in disease risk January-March arising from projected increases in short rains (October-December) precipitation under future climate scenarios. Accurate projections of future precipitation, including a better understanding of potential changes to climatic linkages like the El Niño-Southern Oscillation and Indian Ocean Dipole, will enable meaningful prediction of future RVF risk that can inform disease interventions. Greater consideration of population-level host immunity and climate adaptation behaviors in East Africa (i.e., changing livestock management practices) would also allow for more realistic RVF risk projections.

Although the highlands of East Africa lack the geo-ecological landmarks of Rift Valley fever (RVF) disease hotspots to participate in cyclic RVF epidemics, they have recently reported growing numbers of small RVF clusters. Here, we investigated whether RVF cycling occurred among livestock and humans in the central highlands of Kenya during inter-epidemic periods. A 2-year prospective hospital-based study among febrile patients (March 2022–February 2024) in Murang’a County of Kenya was followed by a cross-sectional human–animal survey. A total of 1468 febrile patients were enrolled at two clinics and sera tested for RVF virus RNA and antiviral antibodies. In the cross-sectional study, humans (n = 282) and livestock (n = 706) from randomly selected households were tested and questionnaire data were used to investigate sociodemographic and environmental risk factors by multivariate logistic regression. No human (n = 1750) or livestock (n = 706) sera tested positive for RVFV RNA. However, 4.4% livestock and 2.0% humans tested positive for anti-RVFV IgG, including 0.27% febrile patients who showed four-fold IgG increase and 2.4% young livestock (p < 0.05, 95% CI) in males (aOR: 4.77, 2.08–12.4), those consuming raw milk (aOR: 5.24, 1.13–17.9), milkers (aOR: 2.69, 1.23–6.36), and participants residing near quarries (aOR: 2.4, 1.08–5.72). In livestock, sheep and goats were less likely to be seropositive (aOR: 0.27, 0.12–0.60) than cattle. The increase in RVF disease activities in the highlands represents a widening geographic dispersal of the virus, and a greater risk of more widespread RVF epidemics in the future.

Although highlands of East Africa lack the geo-ecological landmarks of Rift Valley fever (RVF) disease hotspots to participate in cyclic RVF epidemics, they have recently reported growing numbers of small RVF clusters. Here, we investigated whether RVF cycling occurred among livestock and humans in the central highlands of Kenya during inter-epidemic periods. A 2-year prospective hospital-based study among febrile patients (March 2022 - February 2024) in Murang’a County of Kenya, was followed by a cross-sectional human-animal survey. A total of 1,468 febrile patients were enrolled at two clinics and sera tested for RVF virus RNA and antiviral antibodies. In the cross-sectional study, humans (n=282) and livestock (n=706) from randomly selected households were tested and questionnaire data used to investigate sociodemographic and environmental risk factors by multivariate logistic regression. No human (n=1,750) or livestock (n=706) sera tested positive for RVFV RNA. However, 4.4% livestock and 2.0% humans tested positive for anti-RVFV IgG, including 0.27% febrile patients who showed 4-fold IgG increase and 2.4% young livestock (<12 months old), indicating recent virus exposure. Among humans, odds of RVF exposure increased significantly (P<0.05, 95% CI) in males (aOR:4.77, 2.08–12.4), those consuming raw milk (aOR 5.24,1.13–17.9), milkers (aOR:2.69, 1.23–6.36), and participants residing near quarries (aOR:2.4, 1.08–5.72). In livestock, sheep and goats were less likely to be seropositive (aOR: 0.27, 0.12–0.60) than cattle. The increase in RVF disease activities in the highlands represents widening geographic dispersal of the virus, and greater risk of more widespread RVF epidemics in future.

COVID-19 has presented hospitals with unique challenges. A SHEA Research Network survey showed that 40% reported “limited” or worse levels of personal protective equipment (PPE), and 13% were self-producing PPE to address those deficits, including 3D-printed items. However, we do not know how efficiently, if at all, 3D-printed materials can be disinfected. Additionally, two filaments, PLACTIVE and BIOGUARD, claim to be antimicrobial; they use copper nanocomposites and silver ions to reduce bacterial populations. We assess how PLACTIVE and BIOGUARD may be contaminated and how well they reduce contamination, and how readily Polylactic Acid (PLA), a standard 3D-printed material, may be disinfected. 3D-printed materials, including PLACTIVE and BIOGUARD, are readily contaminated with bacteria that are common in hospitals and can sustain that contamination. Our findings reveal that the levels of contamination on PLACTIVE and BIOGUARD can vary under specific conditions such as layer height or bacterial contact time, sometimes surpassing or falling short of PLA. However, disinfected disks had lower overall CFU averages than those that were not, but the level of disinfection was variable, and bacterial populations recovered hours after disinfection application. Proper disinfection and using appropriate 3D-printed materials are essential to limit bacterial contamination. 3D printers and their products can be invaluable for hospitals, especially when supplies are low, and healthcare worker safety is paramount. Environmental services should be made aware of the presence of antimicrobial 3D-printed materials, and patients should be discouraged from printing their own items for use in hospital environments. IMPORTANCE The COVID-19 pandemic has intensified the demand for personal protective equipment (PPE) in hospitals, prompting the utilization of 3D-printed materials to address shortages. Given the role of environmental contamination in healthcare-associated infections, understanding the potential for bacterial colonization on these materials is crucial. Our findings highlight the importance of proper disinfection practices and material selection in mitigating bacterial contamination, enhancing infection prevention strategies in hospitals, and ensuring the safety of healthcare workers and patients.