Interconnecting global threats: climate change, biodiversity loss, and infectious diseases.

The concurrent pressures of rising global temperatures, rates and incidence of species decline, and emergence of infectious diseases represent an unprecedented planetary crisis. Intergovernmental reports have drawn focus to the escalating climate and biodiversity crises and the connections between them, but interactions among all three pressures have been largely overlooked. Non-linearities and dampening and reinforcing interactions among pressures make considering interconnections essential to anticipating planetary challenges. In this Review, we define and exemplify the causal pathways that link the three global pressures of climate change, biodiversity loss, and infectious disease. A literature assessment and case studies show that the mechanisms between certain pairs of pressures are better understood than others and that the full triad of interactions is rarely considered. Although challenges to evaluating these interactions-including a mismatch in scales, data availability, and methods-are substantial, current approaches would benefit from expanding scientific cultures to embrace interdisciplinarity and from integrating animal, human, and environmental perspectives. Considering the full suite of connections would be transformative for planetary health by identifying potential for co-benefits and mutually beneficial scenarios, and highlighting where a narrow focus on solutions to one pressure might aggravate another.

A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States.

To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.

Disasters collide at the intersection of extreme weather and infectious diseases.

Natural disasters interact to affect the resilience and prosperity of communities and disproportionately affect low income families and communities of colour. However, due to lack of a common theoretical framework, these are rarely quantified. Observing severe weather events (e.g. hurricanes and tornadoes) and epidemics (e.g. COVID-19) unfolding in southeastern US communities led us to conjecture that interactions among catastrophic disturbances might be much more considerable than previously recognized. For instance, hurricane evacuations increase human aggregation, a factor that affects the transmission of acute infections like SARS-CoV-2. Similarly, weather damage to health infrastructure can reduce a community's ability to provide services to people who are ill. As globalization and human population and movement continue to increase and weather events are becoming more intense, such complex interactions are expected to magnify and significantly impact environmental and human health.

Season and prey identity mediate the effect of predators on parasites in rodents: a test of the healthy herds hypothesis.

The healthy herds hypothesis (HHH) suggests that predators decrease parasitism in their prey. Repeated tests of this hypothesis across a range of taxa and ecosystems have revealed significant variation in the effect of predators on parasites in prey. Differences in the response to predators (1) between prey taxa, (2) between seasons, and (3) before and after catastrophic disturbance are common in natural systems, but typically ignored in empirical tests of the HHH. We used a predator exclusion experiment to measure the effect of these heterogeneities on the tri-trophic interaction among predators, parasites and prey. We experimentally excluded mammalian predators from the habitats of hispid cotton rats (Sigmodon hispidus) and cotton mice (Peromyscus gossypinus) and measured the effect of exclusion on gastrointestinal parasites in these rodents. Our experiment spanned multiple seasons and before and after a prescribed burn. We found that the exclusion of the same predators had opposite effects on the parasites of small mammal prey species. Additionally, we found that the effect of mammal exclusion on parasitism differed before versus after fire disturbance. Finally, we saw that the effect of predator exclusion was highly dependent on prey capture season. Significant effects of exclusion emerged primarily in the fall and winter months. The presence of so many different effects in one relatively simple system suggests that predator effects on parasites in prey are highly context dependent.

Traits, phylogeny and host cell receptors predict Ebolavirus host status among African mammals.

We explore how animal host traits, phylogenetic identity and cell receptor sequences relate to infection status and mortality from ebolaviruses. We gathered exhaustive databases of mortality from Ebolavirus after exposure and infection status based on PCR and antibody tests. We performed ridge regressions predicting mortality and infection as a function of traits, phylogenetic eigenvectors and separately host receptor sequences. We found that mortality from Ebolavirus had a strong association to life history characteristics and phylogeny. In contrast, infection status related not just to life history and phylogeny, but also to fruit consumption which suggests that geographic overlap of frugivorous mammals can lead to spread of virus in the wild. Niemann Pick C1 (NPC1) receptor sequences predicted infection statuses of bats included in our study with very high accuracy, suggesting that characterizing NPC1 in additional species is a promising avenue for future work. We combine the predictions from our mortality and infection status models to differentiate between species that are infected and also die from Ebolavirus versus species that are infected but tolerate the virus (possible reservoirs of Ebolavirus). We therefore present the first comprehensive estimates of Ebolavirus reservoir statuses for all known terrestrial mammals in Africa.

Drivers of African Filovirus (Ebola and Marburg) Outbreaks.

Outbreaks of African filoviruses often have high mortality, including more than 11,000 deaths among 28,562 cases during the West Africa Ebola outbreak of 2014-2016. Numerous studies have investigated the factors that contributed to individual filovirus outbreaks, but there has been little quantitative synthesis of this work. In addition, the ways in which the typical causes of filovirus outbreaks differ from other zoonoses remain poorly described. In this study, we quantify factors associated with 45 outbreaks of African filoviruses (ebolaviruses and Marburg virus) using a rubric of 48 candidate causal drivers. For filovirus outbreaks, we reviewed >700 peer-reviewed and gray literature sources and developed a list of the factors reported to contribute to each outbreak (i.e., a "driver profile" for each outbreak). We compare and contrast the profiles of filovirus outbreaks to 200 background outbreaks, randomly selected from a global database of 4463 outbreaks of bacterial and viral zoonotic diseases. We also test whether the quantitative patterns that we observed were robust to the influences of six covariates, country-level factors such as gross domestic product, population density, and latitude that have been shown to bias global outbreak data. We find that, regardless of whether covariates are included or excluded from models, the driver profile of filovirus outbreaks differs from that of background outbreaks. Socioeconomic factors such as trade and travel, wild game consumption, failures of medical procedures, and deficiencies in human health infrastructure were more frequently reported in filovirus outbreaks than in the comparison group. Based on our results, we also present a review of drivers reported in at least 10% of filovirus outbreaks, with examples of each provided.

Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models.

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point-early warning signals (EWS) due to critical slowing down (CSD)-can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.

A Data-driven Horizon Scan of Bacterial Pathogens at the Wildlife-livestock Interface.

Many livestock diseases rely on wildlife for the transmission or maintenance of the pathogen, and the wildlife-livestock interface represents a potential site of disease emergence for novel pathogens in livestock. Predicting which pathogen species are most likely to emerge in the future is an important challenge for infectious disease surveillance and intelligence. We used a machine learning approach to conduct a data-driven horizon scan of bacterial associations at the wildlife-livestock interface for cows, sheep, and pigs. Our model identified and ranked from 76 to 189 potential novel bacterial species that might associate with each livestock species. Wildlife reservoirs of known and novel bacteria were shared among all three species, suggesting that targeting surveillance and/or control efforts towards these reservoirs could contribute disproportionately to reducing spillover risk to livestock. By predicting pathogen-host associations at the wildlife-livestock interface, we demonstrate one way to plan for and prevent disease emergence in livestock.

Land reversion and zoonotic spillover risk.

Deforestation alters wildlife communities and modifies human-wildlife interactions, often increasing zoonotic spillover potential. When deforested land reverts to forest, species composition differences between primary and regenerating (secondary) forest could alter spillover risk trajectory. We develop a mathematical model of land-use change, where habitats differ in their relative spillover risk, to understand how land reversion influences spillover risk. We apply this framework to scenarios where spillover risk is higher in deforested land than mature forest, reflecting higher relative abundance of highly competent species and/or increased human-wildlife encounters, and where regenerating forest has either very low or high spillover risk. We find the forest regeneration rate, the spillover risk of regenerating forest relative to deforested land, and how rapidly regenerating forest regains attributes of mature forest determine landscape-level spillover risk. When regenerating forest has a much lower spillover risk than deforested land, reversion lowers cumulative spillover risk, but instaneous spillover risk peaks earlier. However, when spillover risk is high in regenerating and cleared habitats, landscape-level spillover risk remains high, especially when cleared land is rapidly abandoned then slowly regenerates to mature forest. These results suggest that proactive wildlife management and awareness of human exposure risk in regenerating forests could be important tools for spillover mitigation.

An open-access database of infectious disease transmission trees to explore superspreader epidemiology.

Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.

Overlapping timescales obscure early warning signals of the second COVID-19 wave.

Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.

A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations and deaths.

Short-term forecasts of the dynamics of coronavirus disease 2019 (COVID-19) in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. However, the availability of standardized forecasts and versioned datasets from this period allows for continued work in this area. Here, we introduce the Gaussian infection state space with time dependence (GISST) forecasting model. We evaluate its performance in one to four weeks ahead forecasts of COVID-19 cases, hospital admissions and deaths in the state of California made with official reports of COVID-19, Google's mobility reports and vaccination data available each week. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability and applicability to large multivariate datasets that may prove useful in improving the accuracy of infectious disease forecasts.

Do predators keep prey healthy or make them sicker? A meta-analysis.

Ecological theory suggests that predators can either keep prey populations healthy by reducing parasite burdens or alternatively, increase parasitism in prey. To quantify the overall magnitude and direction of the effect of predation on parasitism in prey observed in practice, we conducted a meta-analysis of 47 empirical studies. We also examined how study attributes, including parasite type and life cycle, habitat type, study design, and whether predators were able to directly consume prey contributed to variation in the predator-prey-parasite interaction. We found that the overall effect of predation on parasitism differed between parasites and parasitoids and that whether consumptive effects were present, and whether a predator was a non-host spreader of parasites, were the most important traits predicting the parasite response. Our results suggest that the mechanistic basis of predator-prey interactions strongly influences the effects of predators on parasites and that these effects, although context dependent, are predictable.

Untangling the evolution of dengue viruses.

The push and pull of dengue virus serotype evolution influences epidemic potential.

Association between tuberculosis in men and social network structure in Kampala, Uganda.

BACKGROUND: Globally, tuberculosis disease (TB) is more common among males than females. Recent research proposes that differences in social mixing by sex could alter infection patterns in TB. We examine evidence for two mechanisms by which social-mixing could increase men's contact rates with TB cases. First, men could be positioned in social networks such that they contact more people or social groups. Second, preferential mixing by sex could prime men to have more exposure to TB cases. METHODS: We compared the networks of male and female TB cases and healthy matched controls living in Kampala, Uganda. Specifically, we estimated their positions in social networks (network distance to TB cases, degree, betweenness, and closeness) and assortativity patterns (mixing with adult men, women, and children inside and outside the household). RESULTS: The observed network consisted of 11,840 individuals. There were few differences in estimates of node position by sex. We found distinct mixing patterns by sex and TB disease status including that TB cases have proportionally more adult male contacts and fewer contacts with children. CONCLUSIONS: This analysis used a network approach to study how social mixing patterns are associated with TB disease. Understanding these mechanisms may have implications for designing targeted intervention strategies in high-burden populations.

Predictors of zoonotic potential in helminths.

Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics conferring a propensity to emergence in other parasite groups (e.g. viruses), the understanding of factors associated with zoonotic potential in helminths remains poor. We applied an investigator-directed learning algorithm to a global dataset of mammal helminth traits to identify factors contributing to spillover of helminths from wild animal hosts into humans. We characterized parasite traits that distinguish between zoonotic and non-zoonotic species with 91% accuracy. Results suggest that helminth traits relating to transmission (e.g. definitive and intermediate hosts) and geography (e.g. distribution) are more important to discriminating zoonotic from non-zoonotic species than morphological or epidemiological traits. Whether or not a helminth causes infection in companion animals (cats and dogs) is the most important predictor of propensity to cause human infection. Finally, we identified helminth species with high modelled propensity to cause zoonosis (over 70%) that have not previously been considered to be of risk. This work highlights the importance of prioritizing studies on the transmission of helminths that infect pets and points to the risks incurred by close associations with these animals. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Characteristics of the 100 largest modern zoonotic disease outbreaks.

Zoonotic disease outbreaks are an important threat to human health and numerous drivers have been recognized as contributing to their increasing frequency. Identifying and quantifying relationships between drivers of zoonotic disease outbreaks and outbreak severity is critical to developing targeted zoonotic disease surveillance and outbreak prevention strategies. However, quantitative studies of outbreak drivers on a global scale are lacking. Attributes of countries such as press freedom, surveillance capabilities and latitude also bias global outbreak data. To illustrate these issues, we review the characteristics of the 100 largest outbreaks in a global dataset (n = 4463 bacterial and viral zoonotic outbreaks), and compare them with 200 randomly chosen background controls. Large outbreaks tended to have more drivers than background outbreaks and were related to large-scale environmental and demographic factors such as changes in vector abundance, human population density, unusual weather conditions and water contamination. Pathogens of large outbreaks were more likely to be viral and vector-borne than background outbreaks. Overall, our case study shows that the characteristics of large zoonotic outbreaks with thousands to millions of cases differ consistently from those of more typical outbreaks. We also discuss the limitations of our work, hoping to pave the way for more comprehensive future studies. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

The ecology of zoonotic parasites in the Carnivora.

The order Carnivora includes over 300 species that vary many orders of magnitude in size and inhabit all major biomes, from tropical rainforests to polar seas. The high diversity of carnivore parasites represents a source of potential emerging diseases of humans. Zoonotic risk from this group may be driven in part by exceptionally high functional diversity of host species in behavioral, physiological, and ecological traits. We review global macroecological patterns of zoonotic parasites within carnivores, and explore the traits of species that serve as hosts of zoonotic parasites. We synthesize theoretical and empirical research and suggest future work on the roles of carnivores as biotic multipliers, regulators, and sentinels of zoonotic disease as timely research frontiers.