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Principal Investigator: Julianne Meisner
Organization: UNIVERSITY OF WASHINGTON
Fiscal Year: 2024
Award: $720,456
Funding agency: National Institute of Allergy and Infectious Diseases
ABSTRACT
Endemic and emerging zoonoses both represent profound threats to public health. While these two disease
systems diverge in many ways, fundamental to both is the importance contact networks in which humans and
animals mix. In STI research and veterinary epidemiology, analysis of human-only and livestock-only networks
have led to significant insights on how transmission occurs, and how best to interrupt it. Yet to our knowledge,
no prior research has modeled a human-animal contact network using empirical data, leaving the benefits of
network epidemiology inaccessible to zoonotic disease research and control. As a result, researchers must as-
sume that humans and animals mix randomly, or rely on weakly-justified assumptions about stratified risk, when
building mathematical models, designing surveillance systems, or planning interventions. There is a critical need
to characterize the structure and dynamics of human-animal contact across a range of settings and disease
systems, in order to reduce the burden of endemic zoonoses and prevent emergence of novel zoonoses. Our
long-term goal is to develop a suite of methods for conducing human-animal network analyses. Our overall
objective is to demonstrate proof-of-principle: that analysis of human-animal contact networks is feasible, and
results in improved inference. Because emergence of novel zoonotic pathogens is a rare event, we will instead
use data from four high-burden endemic zoonoses representing a range of transmission modes: brucellosis, Q
fever, leptospirosis, and anaplasmosis. This ensures we will have adequate power to achieve our objective, and
contributes to the control of high-morbidity, poverty-reinforcing diseases. Across Dornod and Uvurkhangai prov-
inces in Mongolia, we will use an egocentric approach to sampling whereby ego households are randomly se-
lected and asked to name alter households: those whose animal herd mixes with their own. In Aim 1, following
formative qualitative research we will collect empirical human-livestock contact data using surveys and livestock
GPS collars. GPS collars will be placed for five months, during which period network changes will be captured
using a monthly husbandry log (household) and a 24 hour contact diary (individual) completed once per month.
In Aim 2 we will fit a generative network model to the network data gathered in Aim 1. We will simulate synthetic
networks from this generative model, and demonstrate their validity using disease data from real-time qPCR
testing and molecular strain typing. Finally, in Aim 3 we will combine these synthetic networks and disease data
in an epidemic model of disease transmission, separately for each disease, broadly following an SEIR frame-
work. Using these models, we will evaluate the added utility gained by incorporating network structure compared
with assuming random mixing. We expect our contribution to be methods for measuring and modeling human-
animal contact networks. These will provide the necessary foundation for conducting human-animal network
analyses across a range of settings, allowing benefits to accrue through improving the validity of zoonotic disease
modeling and generating broad insights on human-animal network structure.
Terms: <2019 novel corona virus><2019 novel coronavirus><2019-nCoV><A marginale><A phagocytophila><A phagocytophilum><A. marginale><A. phagocytophila><A. phagocytophilum><Airway infections><Anaplasma marginale><Anaplasma phagocytophila><Anaplasma phagocytophilum><Anaplasmosis><Animal Structures><Animals><Bayesian Analysis><Bayesian computation><Bayesian inference><Bayesian network analysis><Bayesian spatial analysis><Bayesian statistical analysis><Bayesian statistical inference><Bayesian statistics><Brucella><Brucellosis><COVID-19 virus><COVID19 virus><Cessation of life><CoV-2><CoV2><Communicable Diseases><Communities><Complex><Coxiella><Cytoecetes phagocytophila><Data><Death><Devices><Disease><Disorder><Ego><Ehrlichia equi><Ehrlichia phagocytophila><Ensure><Environmental Health><Environmental Health Science><Epidemic><Epidemiology><Event><Farm Animal><Foot Diseases><Foundations><Frequencies><Funding><Goals><HGE Agent><Health><Hour><Household><Human><Impoverished><Individual><Infectious Disease Pathway><Infectious Diseases><Infectious Disorder><Interruption><Intervention><Intervention Strategies><Investigators><Left><Leptospira><Leptospirosis><Livestock><Malta Fever><Math Models><Measures><Methodology><Methods><Mission><Modeling><Modern Man><Molecular><Mongolia><Monkey Pox><Monkeypox><Morbidity><Morbidity - disease rate><Mouth Diseases><NIAID><Names><National Institute of Allergy and Infectious Disease><Network Analysis><Oral Cavity Disease><Oral Cavity Disorder><Oral Disease><Oral Disorder><Outcome><Pathogenicity><Pathway Analysis><Pathway interactions><Poverty><Province><Public Health><Q Fever><Qualitative Research><Random Allocation><Random Selection><Reaction Time><Research><Research Personnel><Research Resources><Researchers><Resources><Respiratory Infections><Respiratory Tract Infections><Response RT><Response Time><Risk><Route><SARS corona virus 2><SARS-CO-V2><SARS-COVID-2><SARS-CoV-2><SARS-CoV2><SARS-associated corona virus 2><SARS-associated coronavirus 2><SARS-coronavirus-2><SARS-related corona virus 2><SARS-related coronavirus 2><SARSCoV2><Sampling><Severe Acute Respiratory Coronavirus 2><Severe Acute Respiratory Distress Syndrome CoV 2><Severe Acute Respiratory Distress Syndrome Corona Virus 2><Severe Acute Respiratory Distress Syndrome Coronavirus 2><Severe Acute Respiratory Syndrome CoV 2><Severe Acute Respiratory Syndrome-associated coronavirus 2><Severe Acute Respiratory Syndrome-related coronavirus 2><Severe acute respiratory syndrome associated corona virus 2><Severe acute respiratory syndrome coronavirus 2><Severe acute respiratory syndrome related corona virus 2><Sexually Transmitted Diseases><Sexually Transmitted Disorder><Sexually Transmitted Infection><Structure><Survey Instrument><Surveys><System><Testing><Time><Transmission><Undulant Fever><Venereal Diseases><Venereal Disorders><Venereal Infections><Wuhan coronavirus><Zoonoses><Zoonotic><Zoonotic Infection><combat><communicable disease transmission><coronavirus disease 2019 virus><coronavirus disease-19 virus><cost effectiveness><design><designing><diaries><disease model><disease transmission><disorder model><emerging pathogen><epidemiologic><epidemiological><epidemiological model><generative models><hCoV19><human model><improved><infectious disease transmission><insight><interest><interventional strategy><mathematic model><mathematical model><mathematical modeling><model of human><mouth disorder><mpox><nCoV2><name><named><naming><network models><new pathogen><novel><novel pathogen><pathogen><pathway><prevent><preventing><psychomotor reaction time><risk stratification><sexually acquired infection><statistics><stratify risk><surveillance strategy><transmission process>