Climate variability and outbreaks of infectious diseases in Europe

Several studies provide evidence of a link between vector-borne disease outbreaks and El Niño driven climate anomalies. Less investigated are the effects of the North Atlantic Oscillation (NAO). Here, we test its impact on outbreak occurrences of 13 infectious diseases over Europe during the last fifty years, controlling for potential bias due to increased surveillance and detection. NAO variation statistically influenced the outbreak occurrence of eleven of the infectious diseases. Seven diseases were associated with winter NAO positive phases in northern Europe, and therefore with above-average temperatures and precipitation. Two diseases were associated with the summer or spring NAO negative phases in northern Europe, and therefore with below-average temperatures and precipitation. Two diseases were associated with summer positive or negative NAO phases in southern Mediterranean countries. These findings suggest that there is potential for developing early warning systems, based on climatic variation information, for improved outbreak control and management

Transmission Dynamics and Determinants of Leptospirosis Infection Across Space and Time

Leptospirosis is a major zoonotic source of morbidity and mortality worldwide. While it was traditionally considered a disease of rural farmers and remains a problem in that setting, it has also recently emerged as a public health issue in urban slums. Despite the high burden of leptospirosis around the world, important questions remain about both regional and individual-level transmission dynamics and infection determinants. This dissertation combines epidemiology and advanced quantitative methods to answer key questions about leptospirosis across spatial and temporal scales. My first chapter characterized the spatio-temporal determinants of leptospirosis during an epidemiologic transition in Thailand. Thailand experienced an explosive countrywide emergence of leptospirosis in the late 1990s that established endemic transmission in the agricultural northeastern region of the country. This persistent transmission has proven difficult to manage due to a lack of understanding of the spatial and temporal determinants of infection. I analyzed the relationship between 15 years of weekly reported cases and weather, landscape, and agricultural features at the district level. My goal was to provide information at policy-relevant spatial and temporal scales that could be used to target interventions. I identified that. in contrast to the individual level, rural leptospirosis at the district level is not strongly associated with rice farming. Neither temporal nor spatial analysis identified rice cultivation as a major driver of reported cases. In the absence of rice, I identified rainfall and temperature in recent weeks, as well as other characteristics of the landscape and agricultural system, as determinants of the spatially heterogeneous incidence. District-level infection determinants varied between the emergence and subsequent endemic period. My next two chapters focused on leptospirosis in urban slum residents. Through detailed longitudinal studies in Pau da Lima, an urban slum community in Brazil, our group has identified a number of household risk factors for infection. However, these are a static measure of risk, which is actually a dynamic function of interaction with the contaminated environment. I used GPS tracking to quantify the movement patterns of urban slum residents and their resulting exposure to leptospirosis transmission sources. I identified that urban slum residents spend the majority of their time within 50 meters of the home. Additionally, residents across age and gender groups had the same amount of movement-induced exposure to transmission sources in the environment. I did, however, find significant differences in the activity space—area visited per 24-hour period—between risk groups. Males, who have higher infection rates, have larger activity spaces than females, and the four individuals who became infected during the study had significantly larger activity spaces than other participants. GPS tracking thus allowed me to identify activity space as a novel risk factor for leptospiral infection. Another critical and as yet unanswered question about leptospirosis is whether there is naturally-acquired immunity to reinfection. Measuring this requires accurate infection histories, which we attempt to capture through longitudinal seroincidence studies. However, conventional methods of interpreting paired serology do not take into account the antibody titer decay between samples. Failing to do so sets an artificially high baseline titer from which to calculate a four-fold rise (the standard infection criterion) and reduces the probability an infection event will be recorded as such. Accounting for titer decay in leptospirosis is further complicated by the fact that re-exposure is a common feature of longitudinal data and that the gold standard serologic assay is interval-censored. I developed a flexible likelihood-based method to estimate titer decay using a dataset free of re-exposure. I then applied the decay rate to longitudinal cohort data from urban slum residents. When accounting for titer decay over a six-month interval, infections defined by a four-fold rise increased by a mean of 88%, and over a 12-month period the increase was 763%. Conventional serological interpretations thus severely underestimate leptospirosis infection, with the level of underestimation dependent on the sampling interval. The higher number of cases identified by our modified definition will provide the necessary data to evaluate evidence of acquired immunity due to a pre-existing infection. This dissertation contributes to the understanding of leptospirosis transmission at both the regional and individual scale. It also provides information that can be used to target public health interventions and inform vaccine development. The tools developed herein, while used to answer questions about leptospirosis, have broader applications to a range of questions and pathogens

Estimating occupancy of rare fishes using visual surveys, with a comparison to backpack electrofishing

There is an ongoing need to monitor the status of imperiled fishes in the southeastern United States using effective methods. Visual surveys minimize harm to target species, but few studies have specifically examined their effectiveness compared to other methods or accounted for imperfect species detection. We used snorkel surveys to estimate detection probability and site occupancy for rare fishes in the Toccoa River system of north Georgia. We also carried out backpack electrofishing at a subset of sites to compare detection probabilities for both methods. The probability of detecting Percina aurantiaca (Tangerine Darter) and Etheostoma vulneratum (Wounded Darter) while snorkeling was relatively high, averaging 30% and 22%, respectively, and naive and estimated occupancy rates (i.e., corrected for incomplete species detection) were almost identical for both species. The probability of detecting Erimystax insignis (Blotched Chub) while snorkeling was relatively low (9%), and their estimated occupancy rate (86%) was much higher than we detected in our survey. Detection was negatively related to depth and substrate size for Blotched Chub and positively related to depth for Tangerine Darter. Compared to snorkeling, the probability of detecting a species while backpack electrofishing was higher for Wounded Darter (40%) and comparable for Blotched Chub (11%). Tangerine Darter, however, were never captured while electrofishing even though they occurred at all four sites where both methods were used. Our study demonstrates the successful use of snorkel sampling to estimate occupancy rates of rare fishes in a large, clear southeastern river and illustrates the importance of accounting for imperfect species detection

Changes in historical typhoid transmission across 16 U.S. cities, 1889-1931: Quantifying the impact of investments in water and sewer infrastructures.

Investments in water and sanitation systems are believed to have led to the decline in typhoid fever in developed countries, such that most cases now occur in regions lacking adequate clean water and sanitation. Exploring seasonal and long-term patterns in historical typhoid mortality in the United States can offer deeper understanding of disease drivers. We fit modified Time-series Susceptible-Infectious-Recovered models to city-level weekly mortality counts to estimate seasonal and long-term typhoid transmission. We examined seasonal transmission separately by city and aggregated by water source. Typhoid transmission peaked in late summer/early fall. Seasonality varied by water source, with the greatest variation occurring in cities with reservoirs. We then fit hierarchical regression models to measure associations between long-term transmission and annual financial investments in water and sewer systems. Overall historical $1 per capita ($16.13 in 2017) investments in the water supply were associated with approximately 5% (95% confidence interval: 3-6%) decreases in typhoid transmission, while $1 increases in the overall sewer system investments were associated with estimated 6% (95% confidence interval: 4-9%) decreases. Our findings aid in the understanding of typhoid transmission dynamics and potential impacts of water and sanitation improvements, and can inform cost-effectiveness analyses of interventions to reduce the typhoid burden

Climate variability and outbreaks of infectious diseases in Europe. Sci Rep

Several studies provide evidence of a link between vector-borne disease outbreaks and El Niño driven climate anomalies. Less investigated are the effects of the North Atlantic Oscillation (NAO). Here, we test its impact on outbreak occurrences of 13 infectious diseases over Europe during the last fifty years, controlling for potential bias due to increased surveillance and detection. NAO variation statistically influenced the outbreak occurrence of eleven of the infectious diseases. Seven diseases were associated with winter NAO positive phases in northern Europe, and therefore with above-average temperatures and precipitation. Two diseases were associated with the summer or spring NAO negative phases in northern Europe, and therefore with below-average temperatures and precipitation. Two diseases were associated with summer positive or negative NAO phases in southern Mediterranean countries. These findings suggest that there is potential for developing early warning systems, based on climatic variation information, for improved outbreak control and management

Timing and spatial heterogeneity of leptospirosis transmission in Northeast Thailand

International audienceThailand experienced an explosive country-wide outbreak of leptospirosis in the late 1990s, followed by high endemic transmission. The key barrier to effective control has been a lack of knowledge about the factors driving the timing and spatial distribution of this persistent transmission. We obtained data on weekly leptospirosis incidence in the 320 districts of northeastern Thailand between 2000 and 2014 from the Thai passive notifiable disease surveillance system (R506). We modeled incidence using a spatiotemporally explicit Poisson model and first examined the effects of current and lagged rainfall and temperature (Thai Meteorology Department). We then collected data on environmental covariates—land use (Thai Land Development Department), livestock and irrigation (FAO), NDVI, NDWI, and elevation (Google Earth Engine)—and evaluated their effects on spatial variation in incidence. Between 2000 and 2014, 53,719 cases of leptospirosis were reported in northeastern Thailand. The timing of peak incidence varied between early August and mid-October and did not coincide with periods of rice planting or harvesting. Instead, weekly incidence was strongly associated with rainfall and temperature in the current and two prior weeks. Districts with high flooding propensity (NWDI, OR = 95.24 per 0.01 index point), a high percentage of rice paddies (OR = 1.057 per %), and low cattle density (OR = 0.98 per head) had significantly higher leptospirosis incidence. We also encountered significant spatiotemporal residuals in our model that appear to represent focal outbreaks. Our study found that rainfall and temperature, not specific events in the agricultural cycle, were the main determinants of peak transmission. We also identified specific environmental features associated with persistent high transmission which may serve as targets for prevention. However, in addition to this endemic pattern, outbreaks contribute to the burden of leptospirosis. Understanding the sources of these epidemics will be important for leptospirosis control in this region

Modeling Nation-Wide U.S. Swine Movement Networks at the Resolution of the Individual Premises

Each of the 250 files in the zipped folder represents a single simulated swine shipment network among the counties of the contiguous United States, generated from the U.S. Animal Movement Model (USAMM) version 3. Each file is a tab-delimited .txt file with 15 columns: oCountyId - Origin county FIPS code. dCountyId - Destination county FIPS code. dayOfYear - Day 1 = 1/1, day 365 = 12/31. volume - Number of animals shipped. commodity - swine (s). period - Quarter of the year. oStateAbbr - Origin state abbreviation. dStateAbbr - Destination state abbreviation. producer – If ‘1’, indicates that this shipment is associated with a producer with a swine production health plan, otherwise it is ‘none’. oPremId - Origin premises id. Matches ids from the premises data (FLAPS) file. dPremId - Destination premises id. Matches ids from the premises data (FLAPS) file. oPty - Origin premises type (Frm, farm). oBinnedSize - Binned herd size of origin premises. dPty - Destination premises type (Frm, farm). dBinnedSize - Binned herd size of destination premises. The premises demography data (FLAPS file) consists of the following columns: Id - Premises id matching id columns in network files. County- County FIPS code. X/Y - Projected coordinates. Lat/Lon - Latitude and longitude. type - s (swine farm) s - Number of swine.Download county FIPS code tables at https://www.census.gov/geo/reference/codes/cou.htmlThe spread of transboundary animal diseases (TAD) is a major cause for concern to the worlds agricultural systems. In the dynamics of the spread of TADs between agricultural premises, the movement of livestock between herds plays an important role. Therefore, when constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, incorporating a model component describing between-premises shipments is often a necessity. In the cases when up-to-date and comprehensive shipment data is available, this is a relatively simple task; when data is nonexistent or patchy, researchers need to model the shipments in addition to the disease dynamics, a task that can be complex and time consuming. In the United States (U.S.), livestock shipment data is not generally collected, and when it is, it is not easily available and mostly concerned with between-state shipments. To cover this gap in knowledge and provide insight into the complete shipment networks of livestock animals, the U.S. Animal Movement Model (USAMM) was developed. Previously, USAMM has only modeled cattle shipments, but here we present a version for the U.S. swine shipment network. Like previous versions, USAMM for swine is a Bayesian model fit to premises demography data, and county-level livestock industry variables and the available data of between-state swine movements. The model is then used to simulate, nation-wide networks of both within- and between-state shipments at the level of individual premises for the U.S. swine industry. Here we describe the model in detail and demonstrate its usefulness in a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on TADs and other topics involving the movements of swine in the U.S., we also make a set of 250 simulated swine shipment networks freely available to the research community as a useful surrogate for the missing data.This project was supported by Agriculture and Food Research Initiative Competitive Grant no. 2018-67015-28289 from the USDA National Institute of Food and Agriculture. This work was also supported by USDA Cooperative Agreements: USDA-APHIS-AP20VSCEAH00C049, USDA-APHIS-AP19VSCEAH00C023, and USDA-APHIS-AP17VSCEAH00C012. Data were provided by the U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services. The analyses, views and conclusions contained in this document are those of the authors and should not be interpreted as representing the regulatory opinions, official policies, either expressed or implied, of the USDA-APHIS-Veterinary Services

Data associated with "Modeling U.S. cattle movements until the cows come home: who ships to whom and how many?"

Each file in each zipped folder represents a single simulated cattle shipment network among the counties of the contiguous United States, generated from the U.S. Animal Movement Model (USAMM) version 3. Files in the USAMM-BEEF folder describe shipment networks for beef production only. Files in the USAMM-DAIRY folder describe shipment networks for dairy production only. Each zipped folder contains 1000 files, representing 1000 shipment networks.Livestock movements between agricultural premises is an important pathway for the spread of infectious disease. Data providing details about the origin and destination of shipments, as well as information about the shipment size is an important component of computer models used to formulate mitigation strategies and design surveillance programs. The United States (U.S.) currently lacks a comprehensive database of farm animal shipments, which hinders such efforts. With the U.S. Animal Movement Model (USAMM), earlier work has successfully scaled up from limited data based on interstate certificates of veterinary inspection (CVI) to comprehensive county-level shipment networks at the national scale. In this work, we present three major improvements to earlier versions of USAMM: (1) increased resolution of the model and simulated networks to the level of individual premises; (2) predictions of shipment sizes; (3) taking into account the types and herd sizes of the premises. We fitted parameters in a Bayesian framework to two sets of CVI data consisting of sub-samples of one year's between-state beef and dairy shipments. Through posterior predictive simulation, we then created 1,000 synthetic beef and dairy networks, which we make publicly available to support livestock disease modeling. The simulated networks were validated against summary statistics of the training data as well as out-of-sample CVI data from subsequent years. This new development opens up the possibility of using USAMM in a broader spectrum of applications where information about shipment size and premises identity is necessary and gives novel insights into the U.S. cattle shipment network.This work is supported by funding provided by the U.S. Department of Homeland Security Science and Technology Directorate under contract number D15PC00278. The findings and conclusions in this preliminary publication have not been formally disseminated by the US Department of Agriculture and should not be construed to represent any agency determination or policy. The analyses, views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the regulatory opinions, official policies, either expressed or implied, of the U.S. Department of Homeland Security

Modeling nation-wide US swine movement networks at the resolution of the individual premises

The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between -premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within-and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data.Funding Agencies|USDA National Institute of Food and Agriculture [2018-67015-28289]; USDA Cooperative Agreements [USDA-APHIS-AP20VSCEAH00C049, USDA-APHIS-AP19VSCEAH00C023, USDA-APHIS-AP17VSCEAH 00C012]</p

Modeling US cattle movements until the cows come home: Who ships to whom and how many?

Livestock movements between agricultural premises is an important pathway for the spread of infectious disease. Data providing details about the origin and destination of shipments, as well as information about the shipment size is an important component of computer models used to formulate mitigation strategies and design surveillance programs. The United States (U.S.) currently lacks a comprehensive database of farm animal shipments, which hinders such efforts. With the U.S. Animal Movement Model (USAMM), earlier work has successfully scaled up from limited data based on interstate certificates of veterinary inspection (CVI) to comprehensive county-level shipment networks at the national scale. In this work, we present three major improvements to earlier versions of USAMM: (1) increased resolution of the model and simulated networks to the level of individual premises; (2) predictions of shipment sizes; (3) taking into account the types and herd sizes of the premises. We fitted parameters in a Bayesian framework to two sets of CVI data consisting of sub-samples of one years between-state beef and dairy shipments. Through posterior predictive simulation, we then created 1,000 synthetic beef and dairy networks, which we make publicly available to support livestock disease modeling. The simulated networks were validated against summary statistics of the training data as well as out-of-sample CVI data from subsequent years. This new development opens up the possibility of using USAMM in a broader spectrum of applications where information about shipment size and premises identity is necessary and gives novel insights into the U.S. cattle shipment network.Funding Agencies|U.S. Department of Homeland Security Science and Technology Directorate [D15PC00278]; USDA [AP17VSCEAH00C012]</p