Spatial Spread Of Infectious Diseases Research Articles

A two-phase fluid model for epidemic flow.

We propose a new mathematical and computational modeling framework that incorporates fluid dynamics to study the spatial spread of infectious diseases. We model the susceptible and infected populations as two inviscid fluids which interact with each other. Their motion at the macroscopic level characterizes the progression and spread of the epidemic. To implement the two-phase flow model, we employ high-order numerical methods from computational fluid dynamics. We apply this model to simulate the COVID-19 outbreaks in the city of Wuhan in China and the state of Tennessee in the US. Our modeling and simulation framework allows us to conduct a detailed investigation into the complex spatiotemporal dynamics related to the transmission and spread of COVID-19.

Spatial spread of infectious diseases with conditional vector preferences.

We explore the spatial spread of vector-borne infections with conditional vector preferences, meaning that vectors do not visit hosts at random. Vectors may be differentially attracted toward infected and uninfected hosts depending on whether they carry the pathogen or not. The model is expressed as a system of partial differential equations with vector diffusion. We first study the non-spatial model. We show that conditional vector preferences alone (in the absence of any epidemiological feedback on their population dynamics) may result in bistability between the disease-free equilibrium and an endemic equilibrium. A backward bifurcation may allow the disease to persist even though its basic reproductive number is less than one. Bistability can occur only if both infected and uninfected vectors prefer uninfected hosts. Back to the model with diffusion, we show that bistability in the local dynamics may generate travelling waves with either positive or negative spreading speeds, meaning that the disease either invades or retreats into space. In the monostable case, we show that the disease spreading speed depends on the preference of uninfected vectors for infected hosts, but also on the preference of infected vectors for uninfected hosts under some circumstances (when the spreading speed is not linearly determined). We discuss the implications of our results for vector-borne plant diseases, which are the main source of evidence for conditional vector preferences so far.

Spatiotemporal Transmission Model to Simulate an Interregional Epidemic Spreading

Infectious disease spread is a spatiotemporal process with significant regional differences that can be affected by multiple factors, such as human mobility and manner of contact. From a geographical perspective, the simulation and analysis of an epidemic can promote an understanding of the contagion mechanism and lead to an accurate prediction of its future trends. The existing methods fail to consider the mutual feedback mechanism of heterogeneities between the interregional population interaction and the regional transmission conditions (e.g., contact probability and the effective reproduction number). This disadvantage oversimplifies the transmission process and reduces the accuracy of the simulation results. To fill this gap, a general model considering the spatiotemporal characteristics is proposed, which includes compartment modeling of population categories, flow interaction modeling of population movements, and spatial spread modeling of an infectious disease. Furthermore, the correctness of a theoretical hypothesis for modeling and prediction accuracy of this model was tested with a synthetic data set and a real-world COVID-19 data set in China, respectively. The theoretical contribution of this article was to verify that the interplay of multiple types of geospatial heterogeneities dramatically influences the spatial spread of infectious disease. This model provides an effective method for solving infectious disease simulation problems involving dynamic, complex spatiotemporal processes of geographical elements, such as optimization of lockdown strategies, analyses of the medical resource carrying capacity, and risk assessment of herd immunity from the perspective of geography. Key Words: geospatial heterogeneities, health geography, interregional population interaction, spatiotemporal analysis, transmission modeling.

A review of N‐mixture models

AbstractN‐mixture models were born in 2004 of the necessity to model animal population size from point counts with imperfect detection of individuals, where capture‐recapture methods are infeasible. Initially developed for applications where population size was assumed constant, N‐mixture models were extended in 2011 to include population dynamics, allowing application to populations whose size fluctuates during the study. A further extension in 2014 accommodates populations with multiple “states” such as age class or sex. More recent extensions model spatial movement of animals among habitat patches or the spatial spread of infectious disease in a human population. The core idea underlying this class of models is a hierarchical structure, where the observation model is defined conditional on the model for true abundance. This hierarchy allows researchers to incorporate information about observation and abundance processes, while permitting distinct inferences about elements affecting detection and those affecting abundance. Another benefit of the hierarchical approach is the ability to accommodate many existing sampling protocols such as removal sampling and distance sampling. One drawback to N‐mixture models is that since they estimate both abundance and detection from replicated but unmarked counts, model parameters may not be clearly identifiable. A second drawback is that when observed counts are large, calculating the N‐mixture likelihood is computationally infeasible. This difficulty motivated an approximate likelihood based on the normal approximation to the binomial. The normal approximation provides a diagnostic of parameter estimability based on the closed‐form expression of the Fisher information matrix for a multivariate normal likelihood.This article is categorized under:Data: Types and Structure > Image and Spatial DataStatistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study

Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.

Evaluating the impact of human flow on the spread of infectious diseases

This study explores infectious disease transmission through contact during daily trips between municipalities. We propose an extended susceptible–infectious–recovered model that considers daily movements on the spatial spread of infectious disease. The current model considers two types of movement: long-term movements such as migration and shorter activities completed within a day. We present analytical results using a next-generation matrix and numerical results using actual human flow data, focusing on the number of days it takes for an outbreak from each region to reach the entire area. Our results suggest that the likelihood of infection depends on the ratio of human flow to population rather than the population per se.

Effects of Asymptomatic Infections on the Spatial Spread of Infectious Diseases

Asymptomatic infection and transmission are common for quite a few directly or indirectly transmitted diseases such as COVID-19, cholera, and Zika fever. In this paper, we propose a susceptible-infective-asymptomatic-recovered patch model to address the influence of asymptomatic infections on the spatial spread of infectious diseases. The multipatch basic reproduction number ${\mathcal{R}}_0$ of the model is defined and shown to be a threshold quantity for disease eradication and persistence. Namely, the disease disappears if ${\mathcal{R}}_0\le1$ whereas it spreads otherwise. The monotonicity of ${\mathcal{R}}_0$ with respect to the dispersal rates of the symptomatic and asymptomatic populations is investigated. In particular, for the two-patch case, ${\mathcal{R}}_0$ is either strictly decreasing or strictly increasing or constant in terms of dispersal rates. However, nonmonotonic dependence can occur with movement between three or more patches. The asymptotic profiles of the endemic equilibrium (when it exists) as one or all dispersal rates approach zero or infinity are studied. Interestingly, an increase in infectious dispersal may decrease ${\mathcal{R}}_0$ but increase the number of nonsusceptible individuals. Analytical and numerical results confirm that ignoring asymptomatic carriers not only significantly underestimates the infection risk but also impairs the efficacy of travel restrictions.

Modeling epidemic flow with fluid dynamics.

In this paper, a new mathematical model based on partial differential equations is proposed to study the spatial spread of infectious diseases. The model incorporates fluid dynamics theory and represents the epidemic spread as a fluid motion generated through the interaction between the susceptible and infected hosts. At the macroscopic level, the spread of the infection is modeled as an inviscid flow described by the Euler equation. Nontrivial numerical methods from computational fluid dynamics (CFD) are applied to investigate the model. In particular, a fifth-order weighted essentially non-oscillatory (WENO) scheme is employed for the spatial discretization. As an application, this mathematical and computational framework is used in a simulation study for the COVID-19 outbreak in Wuhan, China. The simulation results match the reported data for the cumulative cases with high accuracy and generate new insight into the complex spatial dynamics of COVID-19.

Determining travel fluxes in epidemic areas.

Infectious diseases attack humans from time to time and threaten the lives and survival of people all around the world. An important strategy to prevent the spatial spread of infectious diseases is to restrict population travel. With the reduction of the epidemic situation, when and where travel restrictions can be lifted, and how to organize orderly movement patterns become critical and fall within the scope of this study. We define a novel diffusion distance derived from the estimated mobility network, based on which we provide a general model to describe the spatiotemporal spread of infectious diseases with a random diffusion process and a deterministic drift process of the population. We consequently develop a multi-source data fusion method to determine the population flow in epidemic areas. In this method, we first select available subregions in epidemic areas, and then provide solutions to initiate new travel flux among these subregions. To verify our model and method, we analyze the multi-source data from mainland China and obtain a new travel flux triggering scheme in the selected 29 cities with the most active population movements in mainland China. The testable predictions in these selected cities show that reopening the borders in accordance with our proposed travel flux will not cause a second outbreak of COVID-19 in these cities. The finding provides a methodology of re-triggering travel flux during the weakening spread stage of the epidemic.

Levels of economic growth and cross-province spread of the COVID-19 in China

BackgroundAfter the first COVID-19 case detected on 8 December 2019 in Wuhan, the Provincial Capital of Hubei, the epidemic quickly spread throughout the whole country of China. Low developmental levels...

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.

Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics.

BackgroundHuman movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global).ObjectiveBig data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local).MethodsWe will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems.ResultsThis project was funded as of May 2020. We have started the data collection, processing, and analysis for the project.ConclusionsResearch findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus.International Registered Report Identifier (IRRID)DERR1-10.2196/24432

How the individual human mobility spatio-temporally shapes the disease transmission dynamics

Human mobility plays a crucial role in the temporal and spatial spreading of infectious diseases. During the past few decades, researchers have been extensively investigating how human mobility affects the propagation of diseases. However, the mechanism of human mobility shaping the spread of epidemics is still elusive. Here we examined the impact of human mobility on the infectious disease spread by developing the individual-based SEIR model that incorporates a model of human mobility. We considered the spread of human influenza in two contrasting countries, namely, Belgium and Martinique, as case studies, to assess the specific roles of human mobility on infection propagation. We found that our model can provide a geo-temporal spreading pattern of the epidemics that cannot be captured by a traditional homogenous epidemic model. The disease has a tendency to jump to high populated urban areas before spreading to more rural areas and then subsequently spread to all neighboring locations. This heterogeneous spread of the infection can be captured by the time of the first arrival of the infection (T_{fi} ), which relates to the landscape of the human mobility characterized by the relative attractiveness. These findings can provide insights to better understand and forecast the disease spreading.

How Does Dispersal Affect the Infection Size?

Human movement facilitates the spatial spread of infectious diseases and poses a serious threat to disease prevention and control. A large number of spatial epidemic models have been proposed and a...

Why did the buffalo cross the park? Resource shortages, but not infections, drive dispersal in female African buffalo (Syncerus caffer).

Dispersal facilitates population health and maintains resilience in species via gene flow. Adult dispersal occurs in some species, is often facultative, and is poorly understood, but has important management implications, particularly with respect to disease spread. Although the role of adult dispersal in spreading disease has been documented, the potential influence of disease on dispersal has received little attention. African buffalo (Syncerus caffer) are wide‐ranging and harbor many pathogens that can affect nearby livestock. Dispersal of adult buffalo has been described, but ecological and social drivers of buffalo dispersal are poorly understood. We investigated drivers of adult buffalo dispersal during a 4‐year longitudinal study at Kruger National Park, South Africa. We monitored the spatial movement of 304 female buffalo in two focal areas using satellite and radio collars, capturing each buffalo every 6 months to assess animal traits and disease status. We used generalized linear mixed models to determine whether likelihood of dispersal for individual female buffalo was influenced by animal traits, herd identity, environmental variables, gastrointestinal parasites, or microparasite infections. The likelihood and drivers of buffalo dispersal varied by herd, area, and year. In the Lower Sabie herd, where resources were abundant, younger individuals were more likely to disperse, with most dispersal occurring in the early wet season and during an unusually dry year, 2009. In the resource‐poor Crocodile Bridge area, buffalo in poor condition were most likely to disperse. Our findings suggest that dispersal of female buffalo is driven by either seasonal (Lower Sabie) or perhaps social (Crocodile Bridge) resource restriction, indicating resource limitation and dispersal decisions are tightly linked for this social ungulate. We found no direct effects of infections on buffalo dispersal, assuaging fears that highly infectious individuals might be more prone to dispersing, which could accelerate the spatial spread of infectious diseases.

Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics

Seasonal variation in human mobility is globally ubiquitous and affects the spatial spread of infectious diseases, but the ability to measure seasonality in human movement has been limited by data availability. Here, we use mobile phone data to quantify seasonal travel and directional asymmetries in Kenya, Namibia, and Pakistan, across a spectrum from rural nomadic populations to highly urbanized communities. We then model how the geographic spread of several acute pathogens with varying life histories could depend on country-wide connectivity fluctuations through the year. In all three countries, major national holidays are associated with shifts in the scope of travel. Within this broader pattern, the relative importance of particular routes also fluctuates over the course of the year, with increased travel from rural to urban communities after national holidays, for example. These changes in travel impact how fast communities are likely to be reached by an introduced pathogen.

Identifying Spatial Invasion of Pandemics on Metapopulation Networks Via Anatomizing Arrival History.

Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: 1) anatomizing the whole spread process among all subpopulations into disjoint componential patches; 2) inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; and 3) recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.

Air-Transportation Networks and the Spatial Spreading of Infectious Diseases

Many studies suggest that air-transportation networks contribute a lot to the spatiotemporal dynamics of infectious diseases. The mobility of individuals over the networks has greatly speeded up the spreading processes of the epidemics and pushed the population in non-epidemic areas into the risk of infection. To figure out the underlying interactions between the air-transportation networks and the transmission of the epidemics, we have (i) analyzed the air-routes and the statistical data on the passenger throughput of the civil aviation of China and (ii) carried out a computer simulation based on the assumption that a novel influenza outbreaks in Southeast Asia. The results show that the topology of the air-transportations networks has a typical structure of heterogeneities. We also find that the epidemics will soon strike China after the initial outbreaks and rapidly spread throughout the whole networks without air-travel restrictions even the reproductive number () is small.

Human mobility and time spent at destination: Impact on spatial epidemic spreading

Host mobility plays a fundamental role in the spatial spread of infectious diseases. Previous theoretical works based on the integration of network theory into the metapopulation framework have shown that the heterogeneities that characterize real mobility networks favor the propagation of epidemics. Nevertheless, the studies conducted so far assumed the mobility process to be either Markovian (in which the memory of the origin of each traveler is lost) or non-Markovian with a fixed traveling time scale (in which individuals travel to a destination and come back at a constant rate). Available statistics however show that the time spent by travelers at destination is characterized by wide fluctuations, ranging from a single day up to several months. Such varying length of stay crucially affects the chance and duration of mixing events among hosts and may therefore have a strong impact on the spread of an emerging disease. Here, we present an analytical and a computational study of epidemic processes on a complex subpopulation network where travelers have memory of their origin and spend a heterogeneously distributed time interval at their destination. Through analytical calculations and numerical simulations we show that the heterogeneity of the length of stay alters the expression of the threshold between local outbreak and global invasion, and, moreover, it changes the epidemic behavior of the system in case of a global outbreak. Additionally, our theoretical framework allows us to study the effect of changes in the traveling behavior in response to the infection, by considering a scenario in which sick individuals do not leave their home location. Finally, we compare the results of our non-Markovian framework with those obtained with a classic Markovian approach and find relevant differences between the two, in the estimate of the epidemic invasion potential, as well as of the timing and the pattern of its spatial spread. These results highlight the importance of properly accounting for host trip duration in epidemic models and open the path to the inclusion of such an additional layer of complexity to the existing modeling approaches.

Can Human Movements Explain Heterogeneous Propagation of Dengue Fever in Cambodia?

BackgroundDetermining the factors underlying the long-range spatial spread of infectious diseases is a key issue regarding their control. Dengue is the most important arboviral disease worldwide and a major public health problem in tropical areas. However the determinants shaping its dynamics at a national scale remain poorly understood. Here we describe the spatial-temporal pattern of propagation of annual epidemics in Cambodia and discuss the role that human movements play in the observed pattern.Methods and FindingsWe used wavelet phase analysis to analyse time-series data of 105,598 hospitalized cases reported between 2002 and 2008 in the 135 (/180) most populous districts in Cambodia. We reveal spatial heterogeneity in the propagation of the annual epidemic. Each year, epidemics are highly synchronous over a large geographic area along the busiest national road of the country whereas travelling waves emanate from a few rural areas and move slowly along the Mekong River at a speed of ∼11 km per week (95% confidence interval 3–18 km per week) towards the capital, Phnom Penh.ConclusionsWe suggest human movements – using roads as a surrogate – play a major role in the spread of dengue fever at a national scale. These findings constitute a new starting point in the understanding of the processes driving dengue spread.