Contact structure, mobility, environmental impact and behaviour: the importance of social forces to infectious disease dynamics and disease ecology

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

Building a pathway to One Health surveillance and response in Asian countries

To detect and respond to emerging diseases more effectively, an integrated surveillance strategy needs to be applied to both human and animal health. Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat. It is not realistic to either invest only in efforts to detect emerging diseases, or to rely solely on event-based surveillance. A comprehensive strategy is needed, concurrently investigating and managing endemic zoonoses, studying evolving diseases which change their character and importance due to influences such as demographic and climatic change, and enhancing understanding of factors which are likely to influence the emergence of new pathogens. This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect. As yet there is no fully formed blueprint that can be applied in Asian countries. Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.

A global examination of ecological niche modeling to predict emerging infectious diseases: a systematic review

Introduction

As emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species' spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types. We conducted a systematic review (PROSPERO: CRD42021251968) with the research question: What is the state of the science and practice of estimating ecological niches via ENM to predict the emergence and spread of vector-borne and/or zoonotic diseases?

Methods

We searched five research databases and eight widely recognized One Health journals between 1995 and 2020. We screened 383 articles at the abstract level (included if study involved vector-borne or zoonotic disease and applied ENM) and 237 articles at the full-text level (included if study described ENM features and modeling processes). Our objectives were to: (1) describe the growth and distribution of studies across the types of infectious diseases, scientific fields, and geographic regions; (2) evaluate the likely effectiveness of the studies to represent ecological niches based on the biotic, abiotic, and mobility framework; (3) explain some potential pitfalls of ENM algorithms and techniques; and (4) provide specific recommendation for future studies on the analysis of ecological niches to predict EIDs.

Results

We show that 99% of studies included mobility factors, 90% modeled abiotic factors with more than half in tropical climate zones, 54% modeled biotic conditions and interactions. Of the 121 studies, 7% include only biotic and mobility factors, 45% include only abiotic and mobility factors, and 45% fully integrated the biotic, abiotic, and mobility data. Only 13% of studies included modifying social factors such as land use. A majority of studies (77%) used well-recognized ENM algorithms (MaxEnt and GARP) and model selection procedures. Most studies (90%) reported model validation procedures, but only 7% reported uncertainty analysis.

Discussion

Our findings bolster ENM to predict EIDs that can help inform the prevention of outbreaks and future epidemics.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier (CRD42021251968).

Age-differentiated incentives for adaptive behavior during epidemics produce oscillatory and chaotic dynamics

Heterogeneity in contact patterns, mortality rates, and transmissibility among and between different age classes can have significant effects on epidemic outcomes. Adaptive behavior in response to the spread of an infectious pathogen may give rise to complex epidemiological dynamics. Here we model an infectious disease in which adaptive behavior incentives, and mortality rates, can vary between two and three age classes. The model indicates that age-dependent variability in infection aversion can produce more complex epidemic dynamics at lower levels of pathogen transmissibility and that those at less risk of infection can still drive complexity in the dynamics of those at higher risk of infection. Policymakers should consider the interdependence of such heterogeneous groups when making decisions.

The lasting influence of Ebola: a qualitative study of community-level behaviors, trust, and perceptions three years after the 2014-16 Ebola epidemic in Liberia

The majority of disease transmission during the 2014-16 West Africa Ebola epidemic was driven by community-based behaviors that proved difficult to change in a social paradigm of misinformation, denial, and deep-seated distrust of government representatives and institutions. In Liberia, perceptions and beliefs about Ebola during and since the epidemic can provide insights useful to public health strategies aimed at improving community preparedness. In this 2018 study, we conducted nine focus groups with Liberians from three communities who experienced Ebola differently, to evaluate behaviors, attitudes, and trust during and after the epidemic. Focus group participants reported that some behaviors adopted during Ebola have persisted (e.g. handwashing and caretaking practices), while others have reverted (e.g. physical proximity and funeral customs); and reported ongoing distrust of the government and denial of the Ebola epidemic. These findings suggest that a lack of trust in the biomedical paradigm and government health institutions persists in Liberia. Future public health information campaigns may benefit from community engagement addressed at understanding beliefs and sources of trust and mistrust in the community to effect behavior change and improve community-level epidemic preparedness.

Assessing spatiotemporal variability in SARS-CoV-2 infection risk for hospital workers using routinely-collected data

The COVID-19 pandemic has emphasised the need to rapidly assess infection risks for healthcare workers within the hospital environment. Using data from the first year of the pandemic, we investigated whether an individual's COVID-19 test result was associated with behavioural markers derived from routinely collected hospital data two weeks prior to a test. The temporal and spatial context of behaviours were important, with the highest risks of infection during the first wave, for staff in contact with a greater number of patients and those with greater levels of activity on floors handling the majority of COVID-19 patients. Infection risks were higher for BAME staff and individuals working more shifts. Night shifts presented higher risks of infection between waves of COVID-19 patients. Our results demonstrate the epidemiological relevance of deriving markers of staff behaviour from electronic records, which extend beyond COVID-19 with applications for other communicable diseases and in supporting pandemic preparedness.

Investigating healthcare worker mobility and patient contacts within a UK hospital during the COVID-19 pandemic

BACKGROUND

Insights into behaviours relevant to the transmission of infections are extremely valuable for epidemiological investigations. Healthcare worker (HCW) mobility and patient contacts within the hospital can contribute to nosocomial outbreaks, yet data on these behaviours are often limited.

METHODS

Using electronic medical records and door access logs from a London teaching hospital during the COVID-19 pandemic, we derive indicators for HCW mobility and patient contacts at an aggregate level. We assess the spatial-temporal variations in HCW behaviour and, to demonstrate the utility of these behavioural markers, investigate changes in the indirect connectivity of patients (resulting from shared contacts with HCWs) and spatial connectivity of floors (owing to the movements of HCWs).

RESULTS

Fluctuations in HCW mobility and patient contacts were identified during the pandemic, with the most prominent changes in behaviour on floors handling the majority of COVID-19 patients. The connectivity between floors was disrupted by the pandemic and, while this stabilised after the first wave, the interconnectivity of COVID-19 and non-COVID-19 wards always featured. Daily rates of indirect contact between patients provided evidence for reactive staff cohorting in response to the number of COVID-19 patients in the hospital.

CONCLUSIONS

Routinely collected electronic records in the healthcare environment provide a means to rapidly assess and investigate behaviour change in the HCW population, and can support evidence based infection prevention and control activities. Integrating frameworks like ours into routine practice will empower decision makers and improve pandemic preparedness by providing tools to help curtail nosocomial outbreaks of communicable diseases.

The emergence of SARS-CoV-2 Omicron subvariants: current situation and future trends

The SARS-CoV-2 Omicron variant (B.1.1.529) has been the most recent variant of concern (VOC) established by the World Health Organization (WHO). Because of its greater infectivity and immune evasion, this variant quickly became the dominant type of circulating SARS-CoV-2 worldwide. Our literature review thoroughly explains the current state of Omicron emergence, particularly by comparing different omicron subvariants, including BA.2, BA.1, and BA.3. Such elaboration would be based on structural variations, mutations, clinical manifestation, transmissibility, pathogenicity, and vaccination effectiveness. The most notable difference between the three subvariants is the insufficiency of deletion (Δ69-70) in the spike protein, which results in a lower detection rate of the spike (S) gene target known as (S) gene target failure (SGTF). Furthermore, BA.2 had a stronger affinity to the human Angiotensin-converting Enzyme (hACE2) receptor than other Omicron sub-lineages. Regarding the number of mutations, BA.1.1 has the most (40), followed by BA.1, BA.3, and BA.3 with 39, 34, and 31 mutations, respectively. In addition, BA.2 and BA.3 have greater transmissibility than other sub-lineages (BA.1 and BA.1.1). These characteristics are primarily responsible for Omicron's vast geographical spread and high contagiousness rates, particularly BA.2 sub-lineages.

Evolution and Ecology of Parasite Avoidance

Parasite avoidance is a host defense that reduces the contact rate with parasites. We investigate avoidance as a primary driver of variation among individuals in the risk of parasitism and the evolution of host-parasite interactions. To bridge mechanistic and taxonomic divides, we define and categorize avoidance by its function and position in the sequence of host defenses. We also examine the role of avoidance in limiting epidemics and evaluate evidence for the processes that drive its evolution. Throughout, we highlight important directions to advance our conceptual and theoretical understanding of the role of avoidance in host-parasite interactions. We emphasize the need to test assumptions and quantify the effect of avoidance independent of other defenses. Importantly, many open questions may be most tractable in host systems that have not been the focus of traditional behavioral avoidance research, such as plants and invertebrates.

Spatio-temporal distribution pattern of COVID-19 in the Northern Italy during the first-wave scenario: The role of the highway network

BACKGROUND

The rapid outbreak of Coronavirus disease 2019 (COVID-19) has posed several challenges to the scientific community. The goal of this paper is to investigate the spread of COVID-19 in Northern Italy during the so-called first wave scenario and to provide a qualitative comparison with the local highway net.

METHODS

Fixed a grid of days from February 27, 2020, the cumulative numbers of infections in each considered province have been compared to sequences of thresholds. As a consequence, a time-evolving classification of the state of danger in terms of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, in view of the smallest threshold overtaken by this comparison, has been obtained for each considered province. The provinces with a significant amount of cases have then been collected into matrices containing only the ones featuring a significant amount of cases.

RESULTS

The time evolution of the classification has then been qualitatively compared to the highway network, to identify similarities and thus linking the rapid spreading of COVID-19 and the highway connections.

CONCLUSIONS

The obtained results demonstrate how the proposed model properly fits with the spread of COVID-19 along with the Italian highway transport network and could be implemented to analyze qualitatively other disease transmissions in different contexts and time periods.

Research Participation Influences Willingness to Reduce Zoonotic Exposure in Uganda

The majority of emerging and re-emerging infectious diseases in people are zoonotic. Despite substantial research in communities adjacent to protected areas with high levels of biodiversity, limited data exist on people's knowledge, attitudes, and practices to avoid exposure to infections from domestic and wild animals. We used a modified grounded-theory framework in QS NVivo to develop a Knowledge, Attitude, and Practices (KAP) survey administered at two time points, KAPT1 (April-July 2016) and KAPT2 (February-May 2018) to participants living at the edge of Kibale National Park, Uganda. We measured the difference in willingness to engage in protective behaviors around zoonotic exposure between an Intervention group (n = 61) and a Comparison group (n = 125). Prior to KAPT1, the Intervention group engaged in a human-centered design (HCD) activity identifying behaviors that reduce zoonotic exposure (March-May 2016). Using a difference-in-difference approach, we compared the Intervention and Comparison groups to assess sustained willingness and use of protective behaviors against domestic and wild animal exposures. At KAPT1, Comparison group participants had a significantly lower (p < 0.05) level of willingness to engage in behaviors that increase exposure to zoonoses from domestic animals; Intervention group participants had a significantly higher (p < 0.01) level of willingness to engage in behaviors that increase exposure to zoonoses from wild animals. At KAPT2, the treatment effect was significant (p < 0.01) for sustained willingness to engage in protective behaviors for domestic animal exposure in the Intervention group. There were no significant differences in practices to avoid domestic and wild animal zoonotic exposure between the Intervention and Comparison groups.

Aggregating human judgment probabilistic predictions of the safety, efficacy, and timing of a COVID-19 vaccine

Safe, efficacious vaccines were developed to reduce the transmission of SARS-CoV-2 during the COVID-19 pandemic. But in the middle of 2020, vaccine effectiveness, safety, and the timeline for when a vaccine would be approved and distributed to the public was uncertain. To support public health decision making, we solicited trained forecasters and experts in vaccinology and infectious disease to provide monthly probabilistic predictions from July to September of 2020 of the efficacy, safety, timing, and delivery of a COVID-19 vaccine. We found, that despite sparse historical data, a linear pool—a combination of human judgment probabilistic predictions—can quantify the uncertainty in clinical significance and timing of a potential vaccine. The linear pool underestimated how fast a therapy would show a survival benefit and the high efficacy of approved COVID-19 vaccines. However, the linear pool did make an accurate prediction for when a vaccine would be approved by the FDA. Compared to individual forecasters, the linear pool was consistently above the median of the most accurate forecasts. A linear pool is a fast and versatile method to build probabilistic predictions of a developing vaccine that is robust to poor individual predictions. Though experts and trained forecasters did underestimate the speed of development and the high efficacy of a SARS-CoV-2 vaccine, linear pool predictions can improve situational awareness for public health officials and for the public make clearer the risks, rewards, and timing of a vaccine.

Community trust of government and non-governmental organizations during the 2014-16 Ebola epidemic in Liberia

The West African Ebola Virus Disease epidemic of 2014-16 cost more than 11,000 lives. Interventions targeting key behaviors to curb transmission, such as safe funeral practices and reporting and isolating the ill, were initially unsuccessful in a climate of fear, mistrust, and denial. Building trust was eventually recognized as essential to epidemic response and prioritized, and trust was seen to improve toward the end of the epidemic as incidence fell. However, little is understood about how and why trust changed during Ebola, what factors were most influential to community trust, and how different institutions might have been perceived under different levels of exposure to the outbreak. In this large-N household survey conducted in Liberia in 2018, we measured self-reported trust over time retrospectively in three different communities with different exposures to Ebola. We found trust was consistently higher for non-governmental organizations than for the government of Liberia across all time periods. Trust reportedly decreased significantly from the start to the peak of the epidemic in the study site of highest Ebola incidence. This finding, in combination with a negative association found between knowing someone infected and trust of both iNGOs and the government, indicates the experience of Ebola may have itself caused a decline of trust in the community. These results suggest that national governments should aim to establish trust when engaging communities to change behavior during epidemics. Further research on the relationship between trust and epidemics may serve to improve epidemic response efficacy and behavior uptake.

Mitigating the externality of diseases of poverty through health aid

Externality exists in healthcare when an individual benefits from others being healthy as it reduces the probability of getting sick from illness. Healthy workers are considered to be the more productive labourers leading to a country's positive economic growth over time. Several research studies have modelled disease transmission and its economic impact on a single country in isolation. We developed a two-country disease-economy model that explores disease transmission and cross-border infection of disease for its impacts. The model includes aspects of a worsening and rapid transmission of disease juxtaposed by positive impacts to the economy from tourism. We found that high friction affects the gross domestic product (GDP) of the lower-income country more than the higher-income country. Health aid from one country to another can substantially help grow the GDP of both countries due to the positive externality of disease reduction. Disease has less impact to both economies if the relative cost of treatment over an alternative (e.g. vaccination) is lower than the baseline value. Providing medical supplies to another country, adopting moderate friction between the countries, and finding treatments with lower costs result in the best scenario to preserve the GDP of both countries.

COVID-19 and environment: a poignant reminder of sustainability in the new normal

The nexus of COVID-19 and environment is conspicuously deep-rooted. The roles of environmental factors in the origin, transmission and spread of COVID-19 and the mutual impact of the pandemic on the global environment have been the two perspectives to view this nexus. The present paper attempts to systematically review the existing literature to understand and explore the linkages of COVID-19 with environment and proposes conceptual frameworks to underline this nexus. Our study indicates a critical role of meteorological factors, ambient air pollutants and wastewater in severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmission-spread dynamics. The study also focuses on the direct and indirect impacts of COVID-19 on the regional and global environment. Most of the indirect environmental effects of COVID-19 were attributed to global human confinement that resulted from the implementation of the pandemic containment measures. This worldwide anthropogenic 'pause' sent ripples to all environmental compartments and presented a unique test bed to identify anthropogenic impacts on the earth's natural systems. The review further addresses emerging sustainability challenges in the new normal and their potential solutions. The situation warrants critical attention to the environment-COVID-19 nexus and innovative sustainable practices to address the ramifications of short- and long-term environmental impacts of the COVID-19 pandemic.

Graphical abstract

Reconstructing contact network structure and cross-immunity patterns from multiple infection histories

Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epidemics with multiple infections on networks and analysed the resulting individual infection time series by introducing similarity metrics between hosts based on their multiple infection histories. We find that, depending on infection multiplicity and network sampling, multiple infection summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining simulation outputs for multiple infection patterns, one can detect immunological interference between pathogens (i.e. the fact that past infections in a host condition future probability of infection). The combination of individual-based simulations and analysis of multiple infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data.

Effect of social isolation in dengue cases in the state of Sao Paulo, Brazil: An analysis during the COVID-19 pandemic

BACKGROUND

Studies have shown that human mobility is an important factor in dengue epidemiology. Changes in mobility resulting from COVID-19 pandemic set up a real-life situation to test this hypothesis. Our objective was to evaluate the effect of reduced mobility due to this pandemic in the occurrence of dengue in the state of São Paulo, Brazil.

METHOD

It is an ecological study of time series, developed between January and August 2020. We use the number of confirmed dengue cases and residential mobility, on a daily basis, from secondary information sources. Mobility was represented by the daily percentage variation of residential population isolation, obtained from the Google database. We modeled the relationship between dengue occurrence and social distancing by negative binomial regression, adjusted for seasonality. We represent the social distancing dichotomously (isolation versus no isolation) and consider lag for isolation from the dates of occurrence of dengue.

RESULTS

The risk of dengue decreased around 9.1% (95% CI: 14.2 to 3.7) in the presence of isolation, considering a delay of 20 days between the degree of isolation and the dengue first symptoms.

CONCLUSIONS

We have shown that mobility can play an important role in the epidemiology of dengue and should be considered in surveillance and control activities.

The effect of eviction moratoria on the transmission of SARS-CoV-2

Massive unemployment during the COVID-19 pandemic could result in an eviction crisis in US cities. Here we model the effect of evictions on SARS-CoV-2 epidemics, simulating viral transmission within and among households in a theoretical metropolitan area. We recreate a range of urban epidemic trajectories and project the course of the epidemic under two counterfactual scenarios, one in which a strict moratorium on evictions is in place and enforced, and another in which evictions are allowed to resume at baseline or increased rates. We find, across scenarios, that evictions lead to significant increases in infections. Applying our model to Philadelphia using locally-specific parameters shows that the increase is especially profound in models that consider realistically heterogenous cities in which both evictions and contacts occur more frequently in poorer neighborhoods. Our results provide a basis to assess eviction moratoria and show that policies to stem evictions are a warranted and important component of COVID-19 control.

Nature's Role in Supporting Health during the COVID-19 Pandemic: A Geospatial and Socioecological Study

The COVID-19 pandemic has brought about unprecedented changes to human lifestyles across the world. The virus and associated social restriction measures have been linked to an increase in mental health conditions. A considerable body of evidence shows that spending time in and engaging with nature can improve human health and wellbeing. Our study explores nature's role in supporting health during the COVID-19 pandemic. We created web-based questionnaires with validated health instruments and conducted spatial analyses in a geographic information system (GIS). We collected data (n = 1184) on people's patterns of nature exposure, associated health and wellbeing responses, and potential socioecological drivers such as relative deprivation, access to greenspaces, and land-cover greenness. The majority of responses came from England, UK (n = 993). We applied a range of statistical analyses including bootstrap-resampled correlations and binomial regression models, adjusting for several potential confounding factors. We found that respondents significantly changed their patterns of visiting nature as a result of the COVID-19 pandemic. People spent more time in nature and visited nature more often during the pandemic. People generally visited nature for a health and wellbeing benefit and felt that nature helped them cope during the pandemic. Greater land-cover greenness within a 250 m radius around a respondent's postcode was important in predicting higher levels of mental wellbeing. There were significantly more food-growing allotments within 100 and 250 m around respondents with high mental wellbeing scores. The need for a mutually-advantageous relationship between humans and the wider biotic community has never been more important. We must conserve, restore and design nature-centric environments to maintain resilient societies and promote planetary health.

Reparations for Black American descendants of persons enslaved in the U.S. and their potential impact on SARS-CoV-2 transmission

Background

In the United States, Black Americans are suffering from a significantly disproportionate incidence of COVID-19. Going beyond mere epidemiological tallying, the potential for racial-justice interventions, including reparations payments, to ameliorate these disparities has not been adequately explored.

Methods

We compared the COVID-19 time-varying R_t curves of relatively disparate polities in terms of social equity (South Korea vs. Louisiana). Next, we considered a range of reproductive ratios to back-calculate the transmission rates βi→j for 4 cells of the simplified next-generation matrix (from which R₀ is calculated for structured models) for the outbreak in Louisiana. Lastly, we considered the potential structural effects monetary payments as reparations for Black American descendants of persons enslaved in the U.S. would have had on pre-intervention βi→j and consequently R₀.

Results

Once their respective epidemics begin to propagate, Louisiana displays R_t values with an absolute difference of 1.3–2.5 compared to South Korea. It also takes Louisiana more than twice as long to bring R_t below 1. Reasoning through the consequences of increased equity via matrix transmission models, we demonstrate how the benefits of a successful reparations program (reflected in the ratio βb→b/βw→w) could reduce R₀ by 31–68%.

Discussion

While there are compelling moral and historical arguments for racial-injustice interventions such as reparations, our study considers potential health benefits in the form of reduced SARS-CoV-2 transmission risk. A restitutive program targeted towards Black individuals would not only decrease COVID-19 risk for recipients of the wealth redistribution; the mitigating effects would also be distributed across racial groups, benefiting the population at large.

Adaptive social contact rates induce complex dynamics during epidemics

Epidemics may pose a significant dilemma for governments and individuals. The personal or public health consequences of inaction may be catastrophic; but the economic consequences of drastic response may likewise be catastrophic. In the face of these trade-offs, governments and individuals must therefore strike a balance between the economic and personal health costs of reducing social contacts and the public health costs of neglecting to do so. As risk of infection increases, potentially infectious contact between people is deliberately reduced either individually or by decree. This must be balanced against the social and economic costs of having fewer people in contact, and therefore active in the labor force or enrolled in school. Although the importance of adaptive social contact on epidemic outcomes has become increasingly recognized, the most important properties of coupled human-natural epidemic systems are still not well understood. We develop a theoretical model for adaptive, optimal control of the effective social contact rate using traditional epidemic modeling tools and a utility function with delayed information. This utility function trades off the population-wide contact rate with the expected cost and risk of increasing infections. Our analytical and computational analysis of this simple discrete-time deterministic strategic model reveals the existence of an endemic equilibrium, oscillatory dynamics around this equilibrium under some parametric conditions, and complex dynamic regimes that shift under small parameter perturbations. These results support the supposition that infectious disease dynamics under adaptive behavior change may have an indifference point, may produce oscillatory dynamics without other forcing, and constitute complex adaptive systems with associated dynamics. Implications for any epidemic in which adaptive behavior influences infectious disease dynamics include an expectation of fluctuations, for a considerable time, around a quasi-equilibrium that balances public health and economic priorities, that shows multiple peaks and surges in some scenarios, and that implies a high degree of uncertainty in mathematical projections.

Social structure defines spatial transmission of African swine fever in wild boar

The spatial spread of infectious disease is determined by spatial and social processes such as animal space use and family group structure. Yet, the impacts of social processes on spatial spread remain poorly understood and estimates of spatial transmission kernels (STKs) often exclude social structure. Understanding the impacts of social structure on STKs is important for obtaining robust inferences for policy decisions and optimizing response plans. We fit spatially explicit transmission models with different assumptions about contact structure to African swine fever virus surveillance data from eastern Poland from 2014 to 2015 and evaluated how social structure affected inference of STKs and spatial spread. The model with social structure provided better inference of spatial spread, predicted that approximately 80% of transmission events occurred within family groups, and that transmission was weakly female-biased (other models predicted weakly male-biased transmission). In all models, most transmission events were within 1.5 km, with some rare events at longer distances. Effective reproductive numbers were between 1.1 and 2.5 (maximum values between 4 and 8). Social structure can modify spatial transmission dynamics. Accounting for this additional contact heterogeneity in spatial transmission models could provide more robust inferences of STKs for policy decisions, identify best control targets and improve transparency in model uncertainty.

The effect of eviction moratoria on the transmission of SARS-CoV-2

Massive unemployment during the COVID-19 pandemic could result in an eviction crisis in US cities. Here we model the effect of evictions on SARS-CoV-2 epidemics, simulating viral transmission within and among households in a theoretical metropolitan area. We recreate a range of urban epidemic trajectories and project the course of the epidemic under two counterfactual scenarios, one in which a strict moratorium on evictions is in place and enforced, and another in which evictions are allowed to resume at baseline or increased rates. We find, across scenarios, that evictions lead to significant increases in infections. Applying our model to Philadelphia using locally-specific parameters shows that the increase is especially profound in models that consider realistically heterogenous cities in which both evictions and contacts occur more frequently in poorer neighborhoods. Our results provide a basis to assess municipal eviction moratoria and show that policies to stem evictions are a warranted and important component of COVID-19 control.

Disease-driven reduction in human mobility influences human-mosquito contacts and dengue transmission dynamics

Heterogeneous exposure to mosquitoes determines an individual’s contribution to vector-borne pathogen transmission. Particularly for dengue virus (DENV), there is a major difficulty in quantifying human-vector contacts due to the unknown coupled effect of key heterogeneities. To test the hypothesis that the reduction of human out-of-home mobility due to dengue illness will significantly influence population-level dynamics and the structure of DENV transmission chains, we extended an existing modeling framework to include social structure, disease-driven mobility reductions, and heterogeneous transmissibility from different infectious groups. Compared to a baseline model, naïve to human pre-symptomatic infectiousness and disease-driven mobility changes, a model including both parameters predicted an increase of 37% in the probability of a DENV outbreak occurring; a model including mobility change alone predicted a 15.5% increase compared to the baseline model. At the individual level, models including mobility change led to a reduction of the importance of out-of-home onward transmission (R, the fraction of secondary cases predicted to be generated by an individual) by symptomatic individuals (up to -62%) at the expense of an increase in the relevance of their home (up to +40%). An individual’s positive contribution to R could be predicted by a GAM including a non-linear interaction between an individual’s biting suitability and the number of mosquitoes in their home (>10 mosquitoes and 0.6 individual attractiveness significantly increased R). We conclude that the complex fabric of social relationships and differential behavioral response to dengue illness cause the fraction of symptomatic DENV infections to concentrate transmission in specific locations, whereas asymptomatic carriers (including individuals in their pre-symptomatic period) move the virus throughout the landscape. Our findings point to the difficulty of focusing vector control interventions reactively on the home of symptomatic individuals, as this approach will fail to contain virus propagation by visitors to their house and asymptomatic carriers.

Human mobility patterns can play an integral role in vector-borne disease dynamics by characterizing an individual’s potential contacts with disease-transmitting vectors. Dengue virus is transmitted by a sedentary vector, but human mobility allows individuals to have contact with mosquitoes at their home and other houses they frequent (their activity space). When accounting for the decreased mobility of symptomatic dengue cases in an agent-based simulation model, however, we found a severely diminished role of the activity space in onward transmission. Those who received the majority of their mosquito contacts outside their home experienced decreases in expected bites and onward transmission when mobility changes were accounted for. Onward transmission was driven by a synergistic relationship between the number of mosquitoes in an individual’s home and their biting suitability, where even those with the highest biting suitability would have limited contribution to transmission given a low number of household mosquitoes. Reactive vector control, which often targets symptomatic cases, could be effective for slowing onward transmission from these cases, but will fail to control virus transmission due to the disproportionate contribution of asymptomatic infections.

Interdisciplinary Approaches to COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has been a significant concern worldwide. The pandemic has demonstrated that public health issues are not merely a health concern but also affect society as a whole. In this chapter, we address the importance of bringing together the world's scientists to find appropriate solutions for controlling and managing the COVID-19 pandemic. Interdisciplinary cooperation, through modern scientific methods, could help to handle the consequences of the pandemic and to avoid the recurrence of future pandemics.

The Impact of Different Types of Violence on Ebola Virus Transmission During the 2018-2020 Outbreak in the Democratic Republic of the Congo

BACKGROUND

Our understanding of the different effects of targeted versus nontargeted violence on Ebola virus (EBOV) transmission in Democratic Republic of the Congo (DRC) is limited.

METHODS

We used time-series data of case counts to compare individuals in Ebola-affected health zones in DRC, April 2018-August 2019. Exposure was number of violent events per health zone, categorized into Ebola-targeted or Ebola-untargeted, and into civilian-induced, (para)military/political, or protests. Outcome was estimated daily reproduction number (Rt) by health zone. We fit linear time-series regression to model the relationship.

RESULTS

Average Rt was 1.06 (95% confidence interval [CI], 1.02-1.11). A mean of 2.92 violent events resulted in cumulative absolute increase in Rt of 0.10 (95% CI, .05-.15). More violent events increased EBOV transmission (P = .03). Considering violent events in the 95th percentile over a 21-day interval and its relative impact on Rt, Ebola-targeted events corresponded to Rt of 1.52 (95% CI, 1.30-1.74), while civilian-induced events corresponded to Rt of 1.43 (95% CI, 1.21-1.35). Untargeted events corresponded to Rt of 1.18 (95% CI, 1.02-1.35); among these, militia/political or ville morte events increased transmission.

CONCLUSIONS

Ebola-targeted violence, primarily driven by civilian-induced events, had the largest impact on EBOV transmission.

Guijarro L, García-Honduvilla N, Álvarez-Mon M, Buján J, García-Gallego S. Dendrimers and Dendritic Materials: From Laboratory to Medical Practice in Infectious Diseases

Infectious diseases are one of the main global public health risks, predominantly caused by viruses, bacteria, fungi, and parasites. The control of infections is founded on three main pillars: prevention, treatment, and diagnosis. However, the appearance of microbial resistance has challenged traditional strategies and demands new approaches. Dendrimers are a type of polymeric nanoparticles whose nanometric size, multivalency, biocompatibility, and structural perfection offer boundless possibilities in multiple biomedical applications. This review provides the reader a general overview about the uses of dendrimers and dendritic materials in the treatment, prevention, and diagnosis of highly prevalent infectious diseases, and their advantages compared to traditional approaches. Examples of dendrimers as antimicrobial agents per se, as nanocarriers of antimicrobial drugs, as well as their uses in gene transfection, in vaccines or as contrast agents in imaging assays are presented. Despite the need to address some challenges in order to be used in the clinic, dendritic materials appear as an innovative tool with a brilliant future ahead in the clinical management of infectious diseases and many other health issues.

Spatial analysis of COVID-19 clusters and contextual factors in New York City

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Proportion positive tests were positively associated with marginalized statuses.

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Low testing and high positivity were associated with public transportation use.

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We recommend testing and health care resources be directed to eastern Brooklyn.

Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives.

Reparations for Black American Descendants of Persons Enslaved in the U.S. and Their Estimated Impact on SARS-CoV-2 Transmission

Background In the United States, Black Americans are suffering from significantly disproportionate incidence and mortality rates of COVID-19. The potential for racial-justice interventions, including reparations payments, to ameliorate these disparities has not been adequately explored. Methods We compared the COVID-19 time-varying R _t curves of relatively disparate polities in terms of social equity (South Korea vs. Louisiana). Next, we considered a range of reproductive ratios to back-calculate the transmission rates β _i→j for 4 cells of the simplified next-generation matrix (from which R ₀ is calculated for structured models) for the outbreak in Louisiana. Lastly, we modeled the effect that monetary payments as reparations for Black American descendants of persons enslaved in the U.S. would have had on pre-intervention β _i→j . Results Once their respective epidemics begin to propagate, Louisiana displays R _t values with an absolute difference of 1.3 to 2.5 compared to South Korea. It also takes Louisiana more than twice as long to bring R _t below 1. We estimate that increased equity in transmission consistent with the benefits of a successful reparations program (reflected in the ratio β _b→b / β _w→w ) could reduce R ₀ by 31 to 68%. Discussion While there are compelling moral and historical arguments for racial injustice interventions such as reparations, our study describes potential health benefits in the form of reduced SARS-CoV-2 transmission risk. As we demonstrate, a restitutive program targeted towards Black individuals would not only decrease COVID-19 risk for recipients of the wealth redistribution; the mitigating effects would be distributed across racial groups, benefitting the population at large.

The urgent need for integrated science to fight COVID-19 pandemic and beyond

The COVID-19 pandemic has become the leading societal concern. The pandemic has shown that the public health concern is not only a medical problem, but also affects society as a whole; so, it has also become the leading scientific concern. We discuss in this treatise the importance of bringing the world's scientists together to find effective solutions for controlling the pandemic. By applying novel research frameworks, interdisciplinary collaboration promises to manage the pandemic's consequences and prevent recurrences of similar pandemics.

HCV incidence is associated with injecting partner age and HCV serostatus mixing in young adults who inject drugs in San Francisco

BACKGROUND

HCV incidence is increasing in the US, notably among younger people who inject drugs (PWID). In a cohort of young adult (age<30 years) PWID in San Francisco we examined whether 'injecting partner mixing' factors, i.e. age of partner and knowledge of their HCV serostatus, were associated with HCV transmission.

METHODS

In 448 susceptible PWID studied prospectively. All participants were asked to report characteristics and behaviors they engaged in with up to 3 injecting partners defined as "people whom you injected the most with" in the past month". These partnerships did not specify that drugs or injecting equipment was shared. HCV incidence was estimated by age of up to 3 injecting partners, categorized as: (i) all <30; (ii) mixed-age (<&≥30); and (iii) all ≥30 years and perceived knowledge of the HCV status of participants' injecting partners' HCV status. Interaction was evaluated between partnership age categories and perceived HCV status of partners.

RESULTS

Between 2006-2018, overall HCV incidence (/100 person years observation [pyo]) was 19.4 (95% CI: 16.4, 22.9). Incidence was highest in those with mixed-age partnerships: 28.5 (95% CI: 21.8, 37.1) and those whose partners were all <30 (23.9; 95% CI: 18.8, 30.4), and lowest if partners were ≥30 (7.5; 95% CI: 4.8, 11.8). In a multivariable analyses adjusting for age, sex (of index), injection frequency, and injection partnership 'monogamy', we found evidence for an interaction: the highest HCV incidence was seen in PWID whose partners were all <30 and who knew at least one of their partners was HCV-positive (58.9, 95% CI: 43.3, 80.0; p<0.01).

CONCLUSIONS

Younger injectors are more likely to acquire HCV from their similarly-aged peers, than older injecting partners. Protective seroadaptive behavior may contribute to reduce incidence. These findings can inform new HCV prevention approaches for young PWID needed to curb the HCV epidemic.

Patterns of human social contact and contact with animals in Shanghai, China

East Asia is as a principal hotspot for emerging zoonotic infections. Understanding the likely pathways for their emergence and spread requires knowledge on human-human and human-animal contacts, but such studies are rare. We used self-completed and interviewer-completed contact diaries to quantify patterns of these contacts for 965 individuals in 2017/2018 in a high-income densely-populated area of China, Shanghai City. Interviewer-completed diaries recorded more social contacts (19.3 vs. 18.0) and longer social contact duration (35.0 vs. 29.1 hours) than self-reporting. Strong age-assortativity was observed in all age groups especially among young participants (aged 7-20) and middle aged participants (25-55 years). 17.7% of participants reported touching animals (15.3% (pets), 0.0% (poultry) and 0.1% (livestock)). Human-human contact was very frequent but contact with animals (especially poultry) was rare although associated with frequent human-human contact. Hence, this densely populated area is more likely to act as an accelerator for human-human spread but less likely to be at the source of a zoonosis outbreak. We also propose that telephone interview at the end of reporting day is a potential improvement of the design of future contact surveys.

Dengue illness impacts daily human mobility patterns in Iquitos, Peru

Background

Human mobility plays a central role in shaping pathogen transmission by generating spatial and/or individual variability in potential pathogen-transmitting contacts. Recent research has shown that symptomatic infection can influence human mobility and pathogen transmission dynamics. Better understanding the complex relationship between symptom severity, infectiousness, and human mobility requires quantification of movement patterns throughout infectiousness. For dengue virus (DENV), human infectiousness peaks 0–2 days after symptom onset, making it paramount to understand human movement patterns from the beginning of illness.

Methodology and principal findings

Through community-based febrile surveillance and RT-PCR assays, we identified a cohort of DENV+ residents of the city of Iquitos, Peru (n = 63). Using retrospective interviews, we measured the movements of these individuals when healthy and during each day of symptomatic illness. The most dramatic changes in mobility occurred during the first three days after symptom onset; individuals visited significantly fewer locations (Wilcoxon test, p = 0.017) and spent significantly more time at home (Wilcoxon test, p = 0.005), compared to when healthy. By 7–9 days after symptom onset, mobility measures had returned to healthy levels. Throughout an individual’s symptomatic period, the day of illness and their subjective sense of well-being were the most significant predictors for the number of locations and houses they visited.

Conclusions/Significance

Our study is one of the first to collect and analyze human mobility data at a daily scale during symptomatic infection. Accounting for the observed changes in human mobility throughout illness will improve understanding of the impact of disease on DENV transmission dynamics and the interpretation of public health-based surveillance data.

Dengue is the most important mosquito-borne viral disease of humans worldwide. Due to the limited mobility of the mosquitoes that transmit dengue virus, human mobility can be a key to both understanding an individual’s exposure to the virus and explaining the spread of dengue throughout a population. Accurate disease models should include human mobility; however, changes in human movement patterns due to the presence of symptoms need to be taken into account. We quantified the impact of symptom presence on human mobility throughout the infectious period by analyzing a dataset on the daily movements of dengue virus infected individuals. Accounting for these changing patterns of mobility will improve understanding of the complex relationship between symptom severity, human movement, and dengue virus transmission. Furthermore, dengue transmission models that incorporate symptom-driven mobility changes can be used to evaluate scenarios and strategies for disease prevention.

Risk and Response to Biological Catastrophe in Lower Income Countries

Natural and intentional biological risks threaten human civilization, both through direct human fatality as well as follow-on effects from a collapse of the just-in-time delivery system that provides food, energy and critical supplies to communities globally. Human beings have multiple innate cognitive biases that systematically impair careful consideration of these risks. Residents of low-income countries, especially those who live in rural areas and are less dependent upon global trade, may be the most resilient communities to catastrophic risks, but low-income countries also present a heightened risk for biological catastrophe. Hotspots for the emergence of new zoonotic diseases are predominantly located in low-income countries. Crowded, poorly supplied healthcare facilities in low-income countries provide an optimal environment for new pathogens to transmit to a next host and adapt for more efficient person-to-person transmission. Strategies to address these risks include overcoming our natural biases and recognizing the importance of these risks, avoiding an over-reliance on developing specific biological countermeasures, developing generalized social and behavioral responses and investing in resilience.

An Ecological Framework for Modeling the Geography of Disease Transmission

Ecological niche modeling (ENM) is widely employed in ecology to predict species’ potential geographic distributions in relation to their environmental constraints and is rapidly becoming the gold-standard method for disease risk mapping. However, given the biological complexity of disease systems, the traditional ENM framework requires reevaluation. We provide an overview of the application of ENM to disease systems and propose a theoretical framework based on the biological properties of both hosts and parasites to produce reliable outputs resembling disease system distributions. Additionally, we discuss the differences between biological considerations when implementing ENM for distributional ecology and epidemiology. This new framework will help the field of disease ecology and applications of biogeography in the epidemiology of infectious diseases.

Infectious diseases greatly impact human health, biodiversity, and global economies, highlighting the need to understand and predict their distributions.

Ecological niche modeling (ENM) was not originally designed to explicitly reconstruct complex biological phenomena such as diseases or parasitism, requiring a reevaluation of the traditional framework.

We provide an integrative ENM framework for disease systems that considers suitable host availability, parasite ecologies, and different scales of modeling.

Disease transmission is driven by factors related to parasite availability and host exposure and susceptibility, which can be incorporated in ENM frameworks.

Rate of intersexual interactions affects injury likelihood in Tasmanian devil contact networks

Identifying the types of contacts that result in disease transmission is important for accurately modeling and predicting transmission dynamics and disease spread in wild populations. We investigated contacts within a population of adult Tasmanian devils (Sarcophilus harrisii) over a 6-month period and tested whether individual-level contact patterns were correlated with accumulation of bite wounds. Bite wounds are important in the spread of devil facial tumor disease, a clonal cancer cell line transmitted through direct inoculation of tumor cells when susceptible and infected individuals bite each other. We used multimodel inference and network autocorrelation models to investigate the effects of individual-level contact patterns, identities of interacting partners, and position within the social network on the propensity to be involved in bite-inducing contacts. We found that males were more likely to receive potentially disease-transmitting bite wounds than females, particularly during the mating season when males spend extended periods mate-guarding females. The number of bite wounds individuals received during the mating season was unrelated to any of the network metrics examined. Our approach illustrates the necessity for understanding which contact types spread disease in different systems to assist the management of this and other infectious wildlife diseases.

Exploring fine-scale human and livestock movement in western Kenya

Human and livestock mobility are key factors in the transmission of several high-burden zoonoses such as rift valley fever and trypanosomiasis, yet our knowledge of this mobility is relatively poor due to difficulty in quantifying population-level movement patterns. Significant variation in the movement patterns of individual hosts means it is necessary to capture their fine-scale mobility in order to gain useful knowledge that can be extrapolated to a population level. Here we explore how the movements of people and their ruminants, and their exposure to various types of land cover, correlate with ruminant ownership and other demographic factors which could affect individual exposure to zoonoses. The study was conducted in Busia County, western Kenya, where the population are mostly subsistence farmers operating a mixed crop/livestock farming system. We used GPS trackers to collect movement data from 26 people and their ruminants for 1 week per individual in July/August 2016, and the study was repeated at the end of the same year to compare movement patterns between the short rainy and dry seasons respectively. We found that during the dry season, people and their ruminants travelled further on trips outside of the household, and that people spent less time on swampland compared to the short rainy season. Our findings also showed that ruminant owners spent longer and travelled further on trips outside the household than non-ruminant owners, and that people and ruminants from poorer households travelled further than people from relatively wealthier households. These results indicate that some individual-level mobility may be predicted by season and by household characteristics such as ruminant ownership and household wealth, which could have practical uses for assessing individual risk of exposure to some zoonoses and for future modelling studies of zoonosis transmission in similar rural areas.

Oyster disease in a changing environment: Decrypting the link between pathogen, microbiome and environment

Shifting environmental conditions are known to be important triggers of oyster diseases. The mechanism(s) behind these synergistic effects (interplay between host, environment and pathogen/s) are often not clear, although there is evidence that shifts in environmental conditions can affect oyster immunity, and pathogen growth and virulence. However, the impact of shifting environmental parameters on the oyster microbiome and how this affects oyster health and susceptibility to infectious pathogens remains understudied. In this review, we summarise the major diseases afflicting oysters with a focus on the role of environmental factors that can catalyse or amplify disease outbreaks. We also consider the potential role of the oyster microbiome in buffering or augmenting oyster disease outbreaks and suggest that a deeper understanding of the oyster microbiome, its links to the environment and its effect on oyster health and disease susceptibility, is required to develop new frameworks for the prevention and management of oyster diseases.

Edge-Based Compartmental Modelling of an SIR Epidemic on a Dual-Layer Static-Dynamic Multiplex Network with Tunable Clustering

The duration, type and structure of connections between individuals in real-world populations play a crucial role in how diseases invade and spread. Here, we incorporate the aforementioned heterogeneities into a model by considering a dual-layer static–dynamic multiplex network. The static network layer affords tunable clustering and describes an individual’s permanent community structure. The dynamic network layer describes the transient connections an individual makes with members of the wider population by imposing constant edge rewiring. We follow the edge-based compartmental modelling approach to derive equations describing the evolution of a susceptible–infected–recovered epidemic spreading through this multiplex network of individuals. We derive the basic reproduction number, measuring the expected number of new infectious cases caused by a single infectious individual in an otherwise susceptible population. We validate model equations by showing convergence to pre-existing edge-based compartmental model equations in limiting cases and by comparison with stochastically simulated epidemics. We explore the effects of altering model parameters and multiplex network attributes on resultant epidemic dynamics. We validate the basic reproduction number by plotting its value against associated final epidemic sizes measured from simulation and predicted by model equations for a number of set-ups. Further, we explore the effect of varying individual model parameters on the basic reproduction number. We conclude with a discussion of the significance and interpretation of the model and its relation to existing research literature. We highlight intrinsic limitations and potential extensions of the present model and outline future research considerations, both experimental and theoretical.

Poverty as an independent risk factor for in-hospital mortality in community-acquired pneumonia: A study in a developing country population

BACKGROUND

Community-acquired pneumonia (CAP) is one of the most severe lower respiratory tract infections with a high in-hospital mortality. The aim of this study was to investigate the socioeconomic and medical risk factors affecting the prognosis of acute pneumonia. The results of this study can mention the value of socioeconomic backgrounds like poverty and illiteracy in clinical practice, even in a well-known biological phenomenon (eg acute pneumonia).

METHODS

In this cross-sectional study, all admitted patients to a tertiary teaching hospital with a diagnosis of community acquired pneumonia in a 12-month period were enrolled. Socioeconomic and demographic characteristics, underlying conditions, clinical manifestations and para-clinical test results at admission registered prospectively. A logistic regression model was conducted using in-hospital mortality as the dependent variable.

RESULTS

A total of 621 patients was included in this study. Among them, 47 patients (7.6%) died during the hospitalisation period. In multiple logistic regression analysis, pleural effusion, a higher CURB-65 score, hyponatremia, hyperglycaemia and poverty (being in the lower economic class) were identified as independent risk factors for in-hospital mortality in community-acquired pneumonia.

CONCLUSION

Numerous factors can influence the prognosis of CAP. In addition to the CURB-65 score and some other medical risk factors, socioeconomic backgrounds can also affect the early outcome in CAP. In this study, being in the lower economic class (as an indicator of poverty) is interpreted as an independent risk factor for a poor prognosis in CAP.

Detection of Emerging Zoonotic Pathogens: An Integrated One Health Approach

The emergence of novel zoonotic pathogens is one of the greatest challenges to global health security. The advent of increasingly sophisticated diagnostics tools has revolutionized our capacity to detect and respond to these health threats more rapidly than ever before. Yet, no matter how sophisticated these tools become, the initial identification of emerging infectious diseases begins at the local community level. It is here that the initial human or animal case resides, and it is here that early pathogen detection would have maximum benefit. Unfortunately, many areas at highest risk of zoonotic disease emergence lack sufficient infrastructure capacity to support robust laboratory diagnostic systems. Multiple factors are essential for pathogen detection networks, including an understanding of the complex sociological and ecological factors influencing disease transmission risk, community engagement, surveillance along high-risk human-animal interfaces, and a skilled laboratory workforce. Here we discuss factors relevant to the emerging disease paradigm, recent technical advances in diagnostic methods, and strategies for comprehensive and sustainable approaches to rapid zoonotic disease detection.