Ottar Bjornstad

Ottar Bjornstad
Pennsylvania State University | Penn State · Center for Infectious Disease Dynamics

Professor

About

310
Publications
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Publications

Publications (310)
Article
Intraspecific interactions can occur through many ways but the mechanisms can be broadly categorized as food exploitation and interference interactions. Identifying how intraspecific interactions impact life history is crucial to accurately predict how population density and structure influence dynamics. However, disentangling the effects of interf...
Article
We develop a new population‐scale model incorporating diapause induction and termination that allows multi‐year predictions of pest dynamics. In addition to predicting phenology and voltinism, the model also allows us to study the degree of overlapping among the life‐stages across time; a quantity not generally predicted by previous models yet a ke...
Article
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Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, parti...
Preprint
Full-text available
Asymptomatic individuals carrying SARS CoV 2 can transmit the virus and contribute to outbreaks of COVID 19, but it is not yet clear how the proportion of asymptomatic infections varies by age and geographic location. Here we use detailed surveillance data gathered during COVID 19 resurgences in six cities of China at the beginning of 2021 to inves...
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Background Vaccination has the proven effectiveness in reducing disease burden. As the emergency program is moving towards completion in many countries, there is a new urgency to appropriately assess the societal health benefits in both the near and longer term. Methods Using an age-structured mathematical infection model, we evaluate the gains ac...
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Anticipating the medium-and long-term trajectory of pathogen emergence has acquired new urgency given the ongoing COVID-19 pandemic. For many human pathogens, the burden of disease depends on age and previous exposure. Understanding the intersection between human population demography and transmission dynamics is therefore critical. Here, we develo...
Preprint
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Anticipating the medium- and long-term trajectory of pathogen emergence has acquired new urgency given the ongoing COVID-19 pandemic. For many human pathogens the burden of disease depends on age and prior exposure. Understanding the intersection between human population demography and transmission dynamics is therefore critical. Here, we develop a...
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More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which...
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Background Until broad vaccination coverage is reached and effective therapeutics are available, controlling population mobility (ie, changes in the spatial location of a population that affect the spread and distribution of pathogens) is one of the major interventions used to reduce transmission of SARS-CoV-2. However, population mobility differs...
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Since COVID-19 spread globally in early 2020 and was declared a pandemic by the World Health Organization (WHO) in March, many countries are managing the local epidemics effectively through intervention measures that limit transmission. The challenges of immigration of new infections into regions and asymptomatic infections remain. Standard determi...
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The development of vaccines has opened a way to lower the public health and societal burden of COVID-19 pandemic. To achieve sustainable gains in the long term, switching the vaccination from one target group to a more diverse portfolio should be planned appropriately. We lay out a general mathematical framework for comparing alternative vaccinatio...
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A Correction to this paper has been published: https://doi.org/10.1038/s41592-021-01079-6.
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Peste des petits ruminants virus (PPRV) causes an infectious disease of high morbidity and mortality among sheep and goats which impacts millions of livestock keepers globally. PPRV transmission risk varies by production system, but a deeper understanding of how transmission scales in these systems and which husbandry practices impact risk is neede...
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Significance The spatial dynamics of infectious-disease spread are driven by the biology of the pathogen and the connectivity patterns among human populations. Models of disease spread often use mobile-phone calling records to calculate the number of trips made among locations in the population, which is used as a proxy for population connectivity....
Article
“I have no idea what’s awaiting me, or what will happen when this all ends. For the moment I know this: there are sick people and they need curing.” ―Albert Camus, The Plague
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We explore a common feature of insect population dynamics, interspecific synchrony, which refers to synchrony in population dynamics among sympatric populations of different species. Such synchrony can arise via several possible mechanisms, including shared environmental effects and shared trophic interactions, but distinguishing the relative impor...
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Apart from its global health importance, measles is a paradigm for the low-dimensional mechanistic understanding of local nonlinear population interactions. A central question for spatio-temporal dynamics is the relative roles of hierarchical spread from large cities to small towns and metapopulation transmission among local small population cluste...
Article
Realistic models of epidemics account for latency, loss of immunity, births and deaths.
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Musk thistle, Carduus nutans, is a major noxious weed throughout its non-native range. The flower head weevil, Rhinocyllus conicus, deemed likely to be a strong candidate for biocontrol based on laboratory tests, has proven variable in its efficacy, suggesting a possible influence of ecological context. To improve our understanding of the dynamics...
Article
“Every day sadder and sadder news of its increase. In the City died this week 7496; and of them, 6102 of the plague. But it is feared that the true number of the dead this week is near 10,000 ....” —Samuel Pepys, 1665
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Responding to an outbreak of a novel coronavirus (agent of COVID-19) in December 2019, China banned travel to and from Wuhan city on 23 January and implemented a national emergency response. We investigated the spread and control of COVID-19 using a unique data set including case reports, human movement and public health interventions. The Wuhan sh...
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Peste des petits ruminants virus (PPRV) causes a contagious disease of high morbidity and mortality in global sheep and goat populations. To better control this disease and inform eradication strategies, an improved understanding of how PPRV transmission risk varies by age is needed. Our study used a piece-wise catalytic model to estimate the age-s...
Preprint
Full-text available
Respiratory illness caused by a novel coronavirus (COVID-19) appeared in China during December 2019. Attempting to contain infection, China banned travel to and from Wuhan city on 23 January and implemented a national emergency response. Here we evaluate the spread and control of the epidemic based on a unique synthesis of data including case repor...
Preprint
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Predictive models for the spatial spread of infectious diseases has received much attention in recent years as tools for the management of infectious diseas outbreaks. Prominently, various versions of the so-called gravity model, borrowed from transportation theory, have been used. However, the original literature suggests that the model has some p...
Preprint
Full-text available
Peste des petits ruminants virus (PPRV) causes a contagious disease of high morbidity and mortality in global sheep and goat populations and leads to approximately $2 billion USD in global annual losses. PPRV is currently targeted by the Food and Agricultural Organization and World Animal Health Organization for global eradication by 2030. To bette...
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A key question in ecology is the relative impact of internal nonlinear dynamics and external perturbations on the long-term trajectories of natural systems. Measles has been analyzed extensively as a paradigm for consumer-resource dynamics due to the oscillatory nature of the host-pathogen life cycle, the abundance of rich data to test theory, and...
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Determining how best to manage an infectious disease outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). However, these two uncertainties are rarely addressed concurrently in epidemic studies. We present an approach...
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Dengue is a climate-sensitive mosquito-borne disease with increasing geographic extent and human incidence. Although the climate–epidemic association and outbreak risks have been assessed using both statistical and mathematical models, local mosquito population dynamics have not been incorporated in a unified predictive framework. Here, we use mosq...
Article
1.Spatial synchrony in population dynamics can be caused by dispersal or spatially correlated variation in environmental factors like weather (Moran effect). Distinguishing between these mechanisms is challenging for natural populations, and the study of dispersal‐induced synchrony in particular has been dominated by theoretical modelling and labor...
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Peste des petits ruminants virus (PPRV) causes a contagious disease of high morbidity and mortality in small ruminant populations globally. Using cross-sectional serosurvey data collected in 2016, our study investigated PPRV seroprevalence and risk factors among sheep, goats and cattle in 20 agropastoral (AP) and pastoral (P) villages in northern T...
Article
Asymmetric interactions among conspecifics can have diverse effects on population dynamics including stabilization, generation of cycles and induction of chaotic fluctuations. A difficult challenge, however, is establishing the link between the impact of asymmetric interactions on life history and the consequences for population dynamics. The small...
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Influenza epidemics vary in intensity from year to year, driven by climatic conditions and by viral antigenic evolution. However, important spatial variation remains unexplained. Here we show predictable differences in influenza incidence among cities, driven by population size and structure. Weekly incidence data from 603 cities in the United Stat...
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A key issue in infectious disease epidemiology is to identify and predict geographic sites of epidemic establishment that contribute to onward spread, especially in the context of invasion waves of emerging pathogens. Conventional wisdom suggests that these sites are likely to be in densely-populated, well-connected areas. For pandemic influenza, h...
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Population structure, spatial diffusion, and climatic conditions mediate the spatiotemporal spread of seasonal influenza in temperate regions. However, much of our knowledge of these dynamics stems from a few well-studied countries, such as the United States (US), and the extent to which this applies in different demographic and climatic environmen...
Data
Mantel tests at the county-level. Mantel and partial Mantel tests using Spearman correlations to detect associations between the phase synchrony of Norwegian, Swedish, and Danish counties and a number of predictor variables. (PDF)
Data
Mantel tests at the county-level, excluding Sweden. Mantel and partial Mantel tests using Spearman correlations to detect associations between the phase synchrony of Norwegian and Danish counties and a number of predictor variables (after excluding the Swedish data). (PDF)
Data
Demographic and environmental covariates across Norway, Sweden and Denmark. County-level measures of population size (A), vapor pressure (B), and temperature (C). For details on how these data were obtained, see S1 Text. (PDF)
Data
Transect variation in population and climate. Mean and standard deviation of city humidity conditions (measured as vapor pressure) (A), population size (B), and temperature (C) within each US transect; red points indicate the corresponding values across Norwegian counties. (PDF)
Data
Mantel tests at the municipality-level. Mantel and partial Mantel tests using Spearman correlations to detect associations between the amplitude and phase synchrony of Norwegian municipalities and a number of additional predictor variables. (PDF)
Data
Phase differences at the municipality-level. Average phase differences between each Norwegian municipality and Oslo. Oslo is indicated by the black square and the area surrounding the capital is enlarged in the inset box for clarity. A positive (negative) phase difference indicates epidemics tend to follow (precede) those in Oslo. (PDF)
Data
All US transects. States in blue were those included in each transect. (PDF)
Data
Transect distribution. Each state is colored according to the proportion of transects in which it is included. (PDF)
Data
Demographic and environmental covariates within Norway. Municipality-level measures of population size (A), specific humidity (B), temperature (C), altitude (D), and airline travel (E). For details on how these data were obtained, see S1 Text. (PDF)
Data
Norwegian municipality-level data. Norwegian ILI time-series that were used in all municipality-level analyses. Each row represents a distinct municipality, and these are ordered from top to bottom by decreasing latitude. Blank regions represent weeks for which we do not have data. (PDF)
Data
County-level data. Norwegian, Swedish, and Danish county time-series. Red trajectories indicate that the data were removed from further analysis due to failure of a Box-Pierce white noise test or the presence of at least two seasons with less than ten cases reported; series that were kept for further analysis are depicted with blue trajectories. Th...
Data
Average epidemic center of mass. Colors indicate the average center of mass of epidemics (in calendar weeks) for each Norwegian, Swedish, and Danish county; counties for which data were discarded are shown in white. Oslo is indicated by the black square. (PDF)
Data
Synchrony of all US transects. Average synchrony estimates for 84 US transects obtained by applying the spatial non-parametric correlation function to the abridged data of (A) ILI trajectories and (B) phase-angle trajectories. Points mark transect centroids and colors represent the corresponding synchrony estimate. Inset panels show the distributio...
Data
Additional data and analysis. Details of additional covariate data and statistical methods referenced in the main text. (PDF)
Data
Mantel tests at the county-level using downsampled data. Mantel and partial Mantel tests using Spearman correlations to detect associations between the phase synchrony of Norwegian, Swedish, and Danish counties and a number of predictor variables (using downsampled Norwegian and Danish data). (PDF)
Data
Discarded municipality-level data. Norwegian ILI time-series that were removed from our analysis due to failure of a Box-Pierce white noise test or the presence of at least two seasons with less than ten cases reported. (PDF)
Data
Synchrony in epidemic timing using downsampled data from Norway and Denmark. Colors indicate the correlation in phase-angles (A) and the average phase difference (B) between each county and Oslo (marked by the black square); counties for which data were discarded are shown in white. (PDF)
Data
Example US transect. Top: blue states on the map were those included in the transect. Bottom: Spatial non-parametric correlation function comparing synchrony between the US transect (blue) and Norway (purple) for (A) ILI trajectories and (B) phase-angle trajectories. Solid lines represent the average synchrony across all regions, dashed lines depic...
Data
Relationship between synchrony and distance in Norway, with and without the 2009/10 pandemic. Spatial non-parametric correlation function for municipalities in Norway with (purple) and without (blue) the inclusion of the 2009/10 pandemic for (A) ILI trajectories and (B) phase-angle trajectories. Solid lines represent the average synchrony across al...
Article
Full-text available
Urbanization and rural-urban migration are two factors driving global patterns of disease and mortality. There is significant concern about their potential impact on disease burden and the effectiveness of current control approaches. Few attempts have been made to increase our understanding of the relationship between urbanization and disease dynam...
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Following successful establishment in Australia and North America, the South African dung beetle (DB) Digitonthophagus gazella was introduced in Brazil in 1990. We investigated the impact of the exotic species on the native community of 42 native DB species using a unique weekly data set spanning 26 years, including 4 years of pre-invasion data. Th...
Chapter
In addition to the mouse malaria data discussed in Sects. 7.7 and 15.3, we consider a coinfection study of FIV in cats (Roy et al. 2009). The experiment showed that disease in cats caused by infection with a virulent feline immunodeficiency viruses (FIVf) can be attenuated by prior infection with strains of lower pathogenicity from cougars.
Chapter
Analysis of epidemic time series is a large endeavor because of the richness of dynamical patterns and plentitude of historical data (Rohani and King 2010). A wide range of tools are used, some of which are borrowed from mainstream statistics other of which are “custom made.” The classic “mainstream” methods belong to two categories: the so-called...
Chapter
Pathogens move in space because of movement of transmission stages and infected/susceptible hosts. Spatial pattern arises from landscape heterogeneities, dispersal and “reaction-diffusion” dynamics among spatially dispersed susceptible, and infected individuals. The probability distribution that governs dispersal distances is often referred to as t...
Chapter
Many infectious disease experiments result in non-independent data because of spatial autocorrelation across fields (such as discussed in Chap. 13), repeated measures on experimental animals (such as the in-host Plasmodium data discussed in Sect. 7.7), or other sources of correlated experimental responses among experimental units (such as the possi...
Chapter
In everyday conversation about contagious maladies, “disease” and “infection” are sometimes used interchangeably. Often this imprecision does not matter. It is however useful to keep in mind that disease strictly speaking refers to symptomology and infection to pathogen/parasite colonization-status. The latent period —the time between a pathogen co...
Chapter
When we fit mechanistic models to data, we have to consider carefully the relationship between the nature of the data versus the nature of the model state variables. For example, when we work with continuous-time S(E)IR models it is important to keep in mind that incidence is not prevalence; so if our data is incidence we will need to do something...
Chapter
Many of the classic studies of the spatiotemporal dynamics of natural enemies and their hosts consider parasitoid-host interactions. Parasitoids represent a fascinating group of insect “infections.” Adults are free-living and lay their eggs in larvae (or eggs) of host insects. Hosts die when the parasitoid(s) complete their development and adults e...
Chapter
Spatial and spatiotemporal data analysis is of great importance in disease dynamics for a number of reasons such as looking for space-time clustering, hot-spot detection, characterizing invasion waves, and quantifying spatial synchrony.
Chapter
The S-language which is the foundation of R was constructed using an “object”-based logic where each object is assigned a “class.” The class, in turn, controls printing, plotting, and summarizing each object.
Chapter
Rabies usually invades a naive host range in spatial waves. This has been documented in great detail for fox rabies in continental Europe and raccoon rabies in the Eastern USA.
Chapter
Host behavior and environmental factors influence disease dynamics in a variety of ways through affecting the parasite/pathogen—the survival of infective stages outside the host, speed of development of free-living stages, etc.; and the host population—changing birth-rates, carrying capacity, social organization, etc. Sometimes such influences have...
Chapter
Chapter 9 discussed how a linear approximation to the perennially nonlinear dynamics of infectious disease can provide important insights on invasion, stability, and resonant periodicity. As remarked by Nisbet and Gurney (1982) more generally, linear approximation can often provide remarkably useful insights for nonlinear ecological systems as long...
Data
Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities
Data
Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities
Book
This book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent case studies in order to explore various conceptual, mathematical, and statistical issues. The dynamics of infectious diseases shows a wide diversity of...
Article
Full-text available
Incidence of whooping cough exhibits variable dynamics across time and space. The periodicity of this disease varies from annual to five years in different geographic regions in both developing and developed countries. Many hypotheses have been put forward to explain this variability such as nonlinearity and seasonality, stochasticity, variable rec...
Article
Full-text available
Evaluating the causes of spatial synchrony in population dynamics in nature is notoriously difficult due to a lack of data and appropriate statistical methods. Here, we use a recently developed method, a multivariate extension of the local indicators of spatial autocorrelation statistic, to map geographic variation in the synchrony of gypsy moth ou...