Using Mathematical Models In A Unified Approach To Predicting The Next Emerging Infectious Disease

Emerging infectious diseases (EIDs) pose a significant threat to human health, global economies, and conservation (Smolinski et al. 2003). They are defined as diseases that have recently increased in incidence (rate of the development of new cases during a given time period), are caused by pathogens that recently moved from one host population to another, have recently evolved, or have recently exhibited a change in pathogenesis (Morse 1993; Krause 1994). Some EIDs threaten global public health through pandemics with large-scale mortality (e.g., HN/AIDS). Others cause smaller outbreaks but have high case fatality ratios or lack effective therapies or vaccines (e.g. Ebola virus or methicillin-resistant Staphylococcus aureus). As a group, EIDs cause hundreds of thousands of deaths each year, and some outbreaks (e.g., SARS, H5N1) have cost the global economy tens of billions of dollars. Emerging diseases also affect plants, livestock, and wildlife and are recognized as a Significant threat to the conservation of biodiversity (Daszak et al. 2000). Approximately 60% of emerging human disease events are zoonotic, and over 75% of these diseases originate in wildlife (Jones et al. 2008). The global response to such epidemics is frequently reactive, and the effectiveness of conventional disease control operations is often too little, too late\u27: With rising globalization, the ease with which diseases spread globally has increased dramatically in recent times. Also, interactions between humans and wildlife have intensified through trade markets, agricultural intensification, logging and mining, and other forms of development that encroach into wild areas. Rapid human population growth, land use change, and change in global trade and travel require a shift toward a proactive, predictive, and preventive approaches for the next zoonotic pandemic

Metapopulation Dynamics Enable Persistence of Influenza A, Including A/H5N1, in Poultry

Thanks to K. Sturm-Ramirez, C. Jessup, J. Rosenthal and the staff of EcoHealth Alliance for feedback. Disclaimer: The contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government.Conceived and designed the experiments: PRH TF RH DZ CSA AG MJM XX TB PD. Performed the experiments: PRH. Analyzed the data: PRH. Contributed reagents/materials/analysis tools: PRH TF RH DZ CSA AG MJM XX TB JHJ PD. Wrote the paper: PRH TF RH DZ CSA AG MJM XX TB JHJ PD.Highly pathogenic influenza A/H5N1 has persistently but sporadically caused human illness and death since 1997. Yet it is still unclear how this pathogen is able to persist globally. While wild birds seem to be a genetic reservoir for influenza A, they do not seem to be the main source of human illness. Here, we highlight the role that domestic poultry may play in maintaining A/H5N1 globally, using theoretical models of spatial population structure in poultry populations. We find that a metapopulation of moderately sized poultry flocks can sustain the pathogen in a finite poultry population for over two years. Our results suggest that it is possible that moderately intensive backyard farms could sustain the pathogen indefinitely in real systems. This fits a pattern that has been observed from many empirical systems. Rather than just employing standard culling procedures to control the disease, our model suggests ways that poultry production systems may be modified.Yeshttp://www.plosone.org/static/editorial#pee

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

bydv-viral-ca-data-output

Data used for Seabloom et al. (2009

bydv-viral-ca-data-output-spatial-subset

This is the subset of data used for cross site comparisons in Seabloom et al. (2009) (e.g., Figures 7 & 8

bydv-viral-ca-data-metadata

File to describe data used in Seabloom et al. (2009

Appendix D. Best candidate logistic regression model, describing one-step-ahead spread of Mycoplasma gallisepticum (MG) in House Finches.

Best candidate logistic regression model, describing one-step-ahead spread of Mycoplasma gallisepticum (MG) in House Finches

Data from: Diversity and composition of viral communities: coinfection of barley and cereal yellow dwarf viruses in California grasslands

Most species host multiple pathogens, yet field studies rarely examine the processes determining pathogen diversity within a single host or the effects of coinfection on pathogen dynamics in natural systems. Coinfection can affect pathogen transmission and virulence. In turn, coinfection can be regulated within hosts by interactions such as cross-protective immunity or at broader spatial scales via vector distributions. Using a general model, we demonstrate that coinfection by a group of vectored pathogens is highest with abundant generalist vectors and weak cross-protection and coinfection- induced mortality. Using these predictions, we investigate the distribution of five coexisting aphid-vectored, viral pathogens (barley and cereal yellow dwarf luteoviruses and poleroviruses) in a native perennial grass (Elymus glaucus) in both space (700 km) and time (4 years). Observed coinfection rates were much higher than expected at random, suggesting that within-host processes exerted weak effects on within-host pathogen diversity. Covariance among viruses in space and time was highest for viral species sharing a vector. Temporal correlation arose from the synchronous invasion of two viruses transmitted by a shared aphid species. On the basis of our modeling and empirical results, we expect that factors external to individual hosts may affect the coinfection dynamics in other communities hosting vectored pathogens

Appendix A. Spread kernel fitting within logistic regression.

Spread kernel fitting within logistic regression

bydv-viral-ca-data-output-temporal-subset

This is the subset of data used for temporal trends in Seabloom et al. (2009) (e.g., Figure 6