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At least 10,000 virus species have the ability to infect humans but, at present, the vast majority are circulating silently in wild mammals 1 , 2 . However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife 3 , 4 . In some cases, this will facilitate zoonotic spillover—a mechanistic link between global environmental change and disease emergence. Here we simulate potential hotspots of future viral sharing, using a phylogeographical model of the mammal–virus network, and projections of geographical range shifts for 3,139 mammal species under climate-change and land-use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, causing the cross-species transmission of their associated viruses an estimated 4,000 times. Owing to their unique dispersal ability, bats account for the majority of novel viral sharing and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Notably, we find that this ecological transition may already be underway, and holding warming under 2 °C within the twenty-first century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking the range shifts of species, especially in tropical regions that contain the most zoonoses and are experiencing rapid warming. Changes in climate and land use will lead to species aggregating in new combinations at high elevations, in biodiversity hotspots and in areas of high human population density in Asia and Africa, driving the cross-species transmission of animal-associated viruses.

The phenomenon of vaccine hesitancy behavior has gained ground over the last three decades, jeopardizing the maintenance of herd immunity. This behavior tends to cluster spatially, creating pockets of unprotected sub-populations that can be hotspots for outbreak emergence. What remains less understood are the social mechanisms that can give rise to spatial clustering in vaccination behavior, particularly at the landscape scale. We focus on the presence of spatial clustering, and aim to mechanistically understand how different social processes can give rise to this phenomenon. In particular, we propose two hypotheses to explain the presence of spatial clustering: (i) social selection , in which vaccine-hesitant individuals share socio-demographic traits, and clustering of these traits generates spatial clustering in vaccine hesitancy; and (ii) social influence , in which hesitant behavior is contagious and spreads through neighboring societies, leading to hesitant clusters. Adopting a theoretical spatial network approach, we explore the role of these two processes in generating patterns of spatial clustering in vaccination behaviors under a range of spatial structures. We find that both processes are independently capable of generating spatial clustering, and the more spatially structured the social dynamics in a society are, the higher spatial clustering in vaccine-hesitant behavior it realizes. Together, we demonstrate that these processes result in unique spatial configurations of hesitant clusters, and we validate our models of both processes with fine-grain empirical data on vaccine hesitancy, social determinants, and social connectivity in the US. Finally, we propose, and evaluate the effectiveness of two novel intervention strategies to diminish hesitant behavior. Our generative modeling approach informed by unique empirical data provides insights on the role of complex social processes in driving spatial heterogeneity in vaccine hesitancy.

Throughout the COVID-19 pandemic, many have worried that the additional burden of seasonal influenza would create a devastating scenario, resulting in overwhelmed healthcare capacities and further loss of life. However, many were pleasantly surprised: the 2020 Southern Hemisphere and 2020–2021 Northern Hemisphere influenza seasons were entirely suppressed. The potential causes and impacts of this drastic public health shift are highly uncertain, but provide lessons about future control of respiratory diseases, especially for the upcoming influenza season.

COVID-19 pandemic-related shifts in healthcare utilization, in combination with trends in non-COVID-19 disease transmission and non-pharmaceutical intervention use, had clear impacts on rates of hospitalization for infectious and chronic diseases. Using a U.S. national healthcare billing database, we estimated the monthly incidence rate ratio of hospitalizations between March 2020 and June 2021 according to 19 ICD-10 diagnostic chapters and 189 subchapters. The majority of primary diagnoses for hospitalization showed an immediate decline in incidence during March 2020. Hospitalizations for reproductive neoplasms, hypertension, and diabetes returned to pre-pandemic levels during late 2020 and early 2021, while others, like those for infectious respiratory disease, did not return to pre-pandemic levels during this period. Our assessment of subchapter-level primary hospitalization codes offers insight into trends among less frequent causes of hospitalization during the COVID-19 pandemic in the U.S. In this study, the authors investigate the impacts of the COVID-19 pandemic on healthcare utilisation in the US. They show that, following an immediate decline, hospitalisations for some conditions returned to pre-pandemic norms by the end of 2020, but for others, including respiratory conditions, this had not occurred by June 2021.

The lower an individual’s socioeconomic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generalizable insights. Here, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social and healthcare-based determinants exacerbates influenza epidemics, and that low socioeconomic status (SES) individuals disproportionately bear the burden of infection. We also identify geographical hotspots of influenza burden in low SES populations, much of which is overlooked in traditional influenza surveillance, and find that these differences are most predicted by variation in susceptibility and access to sickness absenteeism. Our results highlight that the effect of overlapping factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, and targeting public health efforts spatially may be an efficient use of resources to abate inequities. The association between health and socioeconomic prosperity has a long history in the epidemiological literature; addressing health inequities in respiratory-transmitted infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat.

Present estimates suggest there are over 1 million virus species found in mammals alone, with about half a million posing a possible threat to human health. Although previous estimates assume linear scaling between host and virus diversity, we show that ecological network theory predicts a non-linear relationship, produced by patterns of host sharing among virus species. To account for host sharing, we fit a power law scaling relationship for host–virus species interaction networks. We estimate that there are about 40,000 virus species in mammals (including ~10,000 viruses with zoonotic potential), a reduction of two orders of magnitude from present projections of viral diversity. We expect that the increasing availability of host–virus association data will improve the precision of these estimates and their use in the sampling and surveillance of pathogens with pandemic potential. We suggest host sharing should be more widely included in macroecological approaches to estimating biodiversity. A re-analysis of virus diversity in mammals that now takes into account host sharing finds that previous global estimates have been overstated by two orders of magnitude.

Human behavior and infectious disease are related in a dynamic feedback loop. This integral and ubiquitous relationship is often ignored in epidemiological modeling, leading to findings that overlook a crucial element in determining infectious disease transmission and thus have limited utility for public health purposes. Significant past work focuses on one side of the behavior-disease relationship: how contact behavior determines disease transmission. Far less attention has been paid to how disease changes social behavior and the dynamic effects of these behavioral changes on future disease spread. The work that does consider the entire behavior-disease feedback loop is largely theoretical and lacks support from empirical data. In this dissertation, I move this problem forward by characterizing the effects of disease on behavior through empirical data, exemplifying the downstream effects of the identified behavior changes on infectious disease dynamics through epidemiological models, and considering the impacts of population heterogeneity in these behaviors. In chapter 1, I consider how disease physiologically drives behavior change through sickness behaviors. In chapter 2, I characterize the ways that disease modifies behavior socio-politically, through public health policy and information-driven risk perception. In chapter 3, I demonstrate the effects of health inequities that drive population heterogeneity in infectious disease-related behaviors. I achieve this through integration of large-scale empirical data with complex epidemiological and statistical models to address case studies of respiratory-transmitted infectious diseases: influenza and COVID-19. I find that 1) sickness behaviors and absenteeism reduce transmission, and only a small portion of the population must engage in sickness behaviors in order to alter epidemic outcomes; 2) subjective risk perception and state-level policies result in the largest reductions in social contact behavior during the COVID-19 pandemic, and typical behavior-disease models that account for objective risk will overestimate the impacts of behavior change on transmission; and 3) health inequities result in increased influenza among vulnerable populations that are often overlooked by epidemiological surveillance. The COVID-19 pandemic has exemplified that behavior is our first defense against transmission, thus understanding how behavior changes due to disease is a crucial step to understanding epidemiological dynamics and improving public health.

Between 10,000 and 600,000 species of mammal virus are estimated to have the potential to spread in human populations, but the vast majority are currently circulating in wildlife, largely undescribed and undetected by disease outbreak surveillance [1,2,3]. In addition, changing climate and land use drive geographic range shifts in wildlife, producing novel species assemblages and opportunities for viral sharing between previously isolated species [4,5]. In some cases, this will inevitably facilitate spillover into humans [6,7] - a possible mechanistic link between global environmental change and emerging zoonotic disease [8]. Here, we map potential hotspots of viral sharing, using a phylogeographic model of the mammal-virus network, and projections of geographic range shifts for 3,870 mammal species under climate change and land use scenarios for the year 2070. Shifting mammal species are predicted to aggregate at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, sharing novel viruses between 3,000 and 13,000 times. Counter to expectations, holding warming under 2 C within the century does not reduce new viral sharing, due to greater range expansions - highlighting the need to invest in surveillance even in a low-warming future. Most projected viral sharing is driven by diverse hyperreservoirs (rodents and bats) and large-bodied predators (carnivores). Because of their unique dispersal capacity, bats account for the majority of novel viral sharing, and are likely to share viruses along evolutionary pathways that could facilitate future emergence in humans. Our findings highlight the urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking range shifts, especially in tropical countries that harbor the most emerging zoonoses.