Incubation periods impact the spatial predictability of cholera and Ebola outbreaks in Sierra Leone

Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.

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Incubation periods impact the spatial predictability of outbreaks: analysis of cholera and Ebola outbreaks in Sierra Leone

10.1101/19003525 ◽

2019 ◽

Author(s):

Rebecca Kahn ◽

Corey M. Peak ◽

Juan Fernández-Gracia ◽

Alexandra Hill ◽

Amara Jambai ◽

...

Keyword(s):

Infectious Diseases ◽

Sierra Leone ◽

Incubation Period ◽

Population Connectivity ◽

Disease Spread ◽

Long Distance ◽

Spatial Spread ◽

Transmission Parameters ◽

Incubation Periods ◽

The Impact

AbstractForecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel further before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.Significance statementUnderstanding how infectious diseases spread is critical for preventing and containing outbreaks. While advances have been made in forecasting epidemics, much is still unknown. Here we show that the incubation period – the time between exposure to a pathogen and onset of symptoms – is an important factor in predicting spatiotemporal spread of disease and provides one explanation for the different trajectories of the recent Ebola and cholera outbreaks in Sierra Leone. We find that outbreaks of pathogens with longer incubation periods, such as Ebola, tend to have less predictable spread, whereas pathogens with shorter incubation periods, such as cholera, spread in a more predictable, wavelike pattern. These findings have implications for the scale and timing of reactive interventions, such as vaccination campaigns.

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Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

BMC Public Health ◽

10.1186/s12889-021-10260-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Meng-Chun Chang ◽

Rebecca Kahn ◽

Yu-An Li ◽

Cheng-Sheng Lee ◽

Caroline O. Buckee ◽

...

Keyword(s):

Disease Surveillance ◽

Initial Conditions ◽

Human Mobility ◽

Human Movement ◽

Disease Spread ◽

Spatial Spread ◽

Movement Data ◽

Intercity Travel ◽

Travel Restrictions ◽

The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

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Sheep scab spatial distribution: the roles of transmission pathways

Parasites & Vectors ◽

10.1186/s13071-021-04850-y ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Emily Joanne Nixon ◽

Ellen Brooks-Pollock ◽

Richard Wall

Keyword(s):

Disease Transmission ◽

Movement Control ◽

Cost Effective ◽

Infected Sheep ◽

Disease Spread ◽

Psoroptes Ovis ◽

Long Distance ◽

Sheep Scab ◽

The Uk ◽

The Impact

Abstract Background Ovine psoroptic mange (sheep scab) is a highly pathogenic contagious infection caused by the mite Psoroptes ovis. Following 21 years in which scab was eradicated in the UK, it was inadvertently reintroduced in 1972 and, despite the implementation of a range of control methods, its prevalence increased steadily thereafter. Recent reports of resistance to macrocyclic lactone treatments may further exacerbate control problems. A better understanding of the factors that facilitate its transmission are required to allow improved management of this disease. Transmission of infection occurs within and between contiguous sheep farms via infected sheep-to-sheep or sheep–environment contact and through long-distance movements of infected sheep, such as through markets. Methods A stochastic metapopulation model was used to investigate the impact of different transmission routes on the spatial pattern of outbreaks. A range of model scenarios were considered following the initial infection of a cluster of highly connected contiguous farms. Results Scab spreads between clusters of neighbouring contiguous farms after introduction but when long-distance movements are excluded, infection then self-limits spatially at boundaries where farm connectivity is low. Inclusion of long-distance movements is required to generate the national patterns of disease spread observed. Conclusions Preventing the movement of scab infested sheep through sales and markets is essential for any national management programme. If effective movement control can be implemented, regional control in geographic areas where farm densities are high would allow more focussed cost-effective scab management. Graphical Abstract

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Travel distance and human movement predict paths of emergence and spatial spread of chikungunya in Thailand

Epidemiology and Infection ◽

10.1017/s0950268818001917 ◽

2018 ◽

Vol 146 (13) ◽

pp. 1654-1662 ◽

Cited By ~ 1

Author(s):

S. Chadsuthi ◽

B. M. Althouse ◽

S. Iamsirithaworn ◽

W. Triampo ◽

K. H. Grantz ◽

...

Keyword(s):

Human Movement ◽

Spatial Distance ◽

Spatiotemporal Pattern ◽

Rubber Plantation ◽

Population Mobility ◽

Long Distance ◽

Spatial Spread ◽

Incidence Data ◽

Southern Thailand ◽

The Impact

AbstractHuman movement contributes to the probability that pathogens will be introduced to new geographic locations. Here we investigate the impact of human movement on the spatial spread of Chikungunya virus (CHIKV) in Southern Thailand during a recent re-emergence. We hypothesised that human movement, population density, the presence of habitat conducive to vectors, rainfall and temperature affect the transmission of CHIKV and the spatiotemporal pattern of cases seen during the emergence. We fit metapopulation transmission models to CHIKV incidence data. The dates at which incidence in each of 151 districts in Southern Thailand exceeded specified thresholds were the target of model fits. We confronted multiple alternative models to determine which factors were most influential in the spatial spread. We considered multiple measures of spatial distance between districts and adjacency networks and also looked for evidence of long-distance translocation (LDT) events. The best fit model included driving-distance between districts, human movement, rubber plantation area and three LDT events. This work has important implications for predicting the spatial spread and targeting resources for control in future CHIKV emergences. Our modelling framework could also be adapted to other disease systems where population mobility may drive the spatial advance of outbreaks.

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Lockdown related travel behavior undermines the containment of SARS-CoV-2

10.1101/2020.10.22.20217752 ◽

2020 ◽

Author(s):

Nishant Kishore ◽

Rebecca Kahn ◽

Pamela P. Martinez ◽

Pablo M. De Salazar ◽

Ayesha S. Mahmud ◽

...

Keyword(s):

Rural Areas ◽

Travel Behavior ◽

Human Mobility ◽

Unintended Consequences ◽

Disease Spread ◽

Mobility Patterns ◽

Long Distance ◽

Mobility Data ◽

Travel Restrictions ◽

The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

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2367. Selecting Testing Frequency for Estimation of Incubation Periods: A Simulation Study Based on Clostridioides difficile Observations

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.2045 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S816-S816

Author(s):

Brigid Wilson ◽

Mustafa S Ascha ◽

Justin O’Hagan ◽

Curtis Donskey

Keyword(s):

Incubation Period ◽

Simulation Study ◽

Symptom Onset ◽

Pathogen Transmission ◽

Sample Sizes ◽

Clostridioides Difficile ◽

Testing Frequency ◽

Incubation Periods ◽

Interval Censored ◽

The Impact

Abstract Background Estimates of the incubation period (time between pathogen transmission and symptom onset) for an infection inform infection control and prevention measures. However, observation of the exact transmission and onset times rarely occurs and “coarse,” or doubly interval-censored, data about these exact times are typically used for estimation. The effect of coarseness on the required number of symptomatic cases and the uncertainty of the estimates is unknown, prompting a simulation study informed by data from an investigation of the incubation period of Clostridioides difficile. Methods We simulated incubation period data assuming a log-normal distribution, a true median incubation period of 7 days, and a standard deviation of 1 day for sample sizes of 50 to 300 symptomatic cases. For each sample size, we simulated 1000 datasets and examined the impact of testing frequencies, considering intervals between tests of 0.25 to 2 times the median incubation period (1.75 to 14 days) about both transmission and symptom onset times. With these doubly interval-censored observed values, we fit accelerated failure time models to estimate the median incubation time and its 95% confidence interval (CI). Comparing the coverage of the true median and the widths of the CIs, we summarized simulation results across sample sizes and testing frequencies. Results Model results from all combinations of sample sizes and testing frequencies yielded median incubation period CIs close to the target 95% coverage level (Figure 1). The width of the 95% CI about the median decreased with larger sample sizes and shorter times between tests (Figure 2). Thus, similar estimates and confidence intervals would be observed from 100 symptomatic cases with a testing frequency of 3.5 days as from 200 symptomatic cases tested every 14 days. Conclusion The frequency of testing is a key factor in planning studies to estimate incubation periods for infectious diseases. To achieve a desired degree of certainty in estimation, increased frequency of testing can reduce the number of symptomatic cases required. We showed that simulations can assist in planning natural history studies, and these methods could be extended to include population data (e.g., transmission incidence) and cost constraints. Disclosures All authors: No reported disclosures.

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SPATIOTEMPORAL TRANSMISSION DYNAMICS OF RECENT EBOLA OUTBREAK IN SIERRA LEONE, WEST AFRICA: IMPACT OF CONTROL MEASURES

Journal of Biological System ◽

10.1142/s0218339017500176 ◽

2017 ◽

Vol 25 (03) ◽

pp. 369-397 ◽

Cited By ~ 1

Author(s):

PARIMITA ROY ◽

RANJIT KUMAR UPADHYAY

Keyword(s):

West Africa ◽

Sierra Leone ◽

Epidemic Model ◽

Control Measure ◽

Reaction Diffusion ◽

Model System ◽

Control Measures ◽

Transmission Dynamics ◽

Spatial Spread ◽

The Impact

In this paper, we have formulated a compartmental epidemic model with exponentially decaying transmission rates to understand the Ebola transmission dynamics and study the impact of control measures to basic public health. The epidemic model exhibits two equilibria, namely, the disease-free and unique endemic equilibria. We have calculated the basic reproduction number through next generation matrix and investigated the spatial spread of the epidemic via reaction–diffusion modeling. Instead of fitting the model to the observed pattern of spread, we have used previously estimated parameter values and examined the efficacy of predictions of the designed model vis-à-vis the pattern of spread observed in Sierra Leone, West Africa. Further, we conducted a sensitivity analysis to determine the extent to which improvement in predictions is achievable through better parameterization.We performed numerical simulations with and without control measure for the designed model system. A significant reduction in infection and death cases were observed when proper control measures are incorporated in the model system. Two-dimensional simulation experiments show that infectious population and the number of deaths will increase up to one and a half years without control, but it will decline after two years. We have reported the numerical results, and it closely matches with the real situation in Sierra Leone.

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STRUCTURED INFLUENZA MODEL FOR META-POPULATION

International Journal of Biomathematics ◽

10.1142/s1793524509000790 ◽

2009 ◽

Vol 02 (04) ◽

pp. 525-541 ◽

Cited By ~ 1

Author(s):

MARIJA ŽIVKOVIĆ GOJOVIĆ ◽

DONG LIANG ◽

JIANHONG WU

Keyword(s):

Control Strategies ◽

Influenza Pandemic ◽

Influenza Infection ◽

Control Measures ◽

Disease Spread ◽

Behavior Changes ◽

Spatial Spread ◽

Structured Population ◽

Age Structured ◽

The Impact

We present a mathematical model parameterized to simulate the 1918 pandemic and modified to account for today's achievements in medical care and technology. Our goal is to use the model with carefully selected parameters to analyze and simulate different scenarios in a changing environment including behavior changes and reduction of mobility as the disease progresses. The model is structured by the disease age, representing the time elapsed since the exposure to influenza infection, and most of the parameters used in this study are thus disease-age dependent. We also consider the case where an influenza pandemic affects two distinct regions, connected only through controlled mobility. We evaluate the influence of different control measures on temporal patterns of disease dynamics and consider the impact of the movement of disease age structured population on spatial spread. A special example is examined that considers different scenarios of disease spread between Canada and USA when different border control strategies are implemented.

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Climate and the Global Spread and Impact of Bananas’ Black Leaf Sigatoka Disease

Atmosphere ◽

10.3390/atmos11090947 ◽

2020 ◽

Vol 11 (9) ◽

pp. 947

Author(s):

Eric Strobl ◽

Preeya Mohan

Keyword(s):

Climatic Factors ◽

Disease Incidence ◽

Disease Spread ◽

Climate Data ◽

Black Sigatoka ◽

Long Distance ◽

Leaf Disease ◽

Global Spread ◽

The Impact ◽

First Time

While Black Sigatoka Leaf Disease (Mycosphaerella fijiensis) has arguably been the most important pathogen affecting the banana industry over the past 50 years, there are no quantitative estimates of what risk factors determine its spread across the globe, nor how its spread has affected banana producing countries. This study empirically models the disease spread across and its impact within countries using historical spread timelines, biophysical models, local climate data, and country level agricultural data. To model the global spread a empirical hazard model is employed. The results show that the most important factor affecting first time infection of a country is the extent of their agricultural imports, having increased first time disease incidence by 69% points. In contrast, long distance dispersal due to climatic factors only raised this probability by 0.8% points. The impact of disease diffusion within countries once they are infected is modelled using a panel regression estimator. Findings indicate that under the right climate conditions the impact of Black Sigatoka Leaf Disease can be substantial, currently resulting in an average 3% reduction in global annual production, i.e., a loss of yearly revenue of about USD 1.6 billion.

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Accent Policy and Accent Modification Enterprises as Potential Indicators of Intercultural Power Relations: A Call for an Updated Research Agenda

Perspectives on Global Issues in Communication Sciences and Related Disorders ◽

10.1044/gics3.1.22 ◽

2013 ◽

Vol 3 (1) ◽

pp. 22-33 ◽

Cited By ~ 1

Author(s):

Brynne D. Ovalle ◽

Rahul Chakraborty

Keyword(s):

International Relations ◽

Identity Construction ◽

Social Theory ◽

Power Relations ◽

Linguistic Diversity ◽

Cultural Organization ◽

Using Data ◽

The Individual ◽

The Impact ◽

The Relationship

This article has two purposes: (a) to examine the relationship between intercultural power relations and the widespread practice of accent discrimination and (b) to underscore the ramifications of accent discrimination both for the individual and for global society as a whole. First, authors review social theory regarding language and group identity construction, and then go on to integrate more current studies linking accent bias to sociocultural variables. Authors discuss three examples of intercultural accent discrimination in order to illustrate how this link manifests itself in the broader context of international relations (i.e., how accent discrimination is generated in situations of unequal power) and, using a review of current research, assess the consequences of accent discrimination for the individual. Finally, the article highlights the impact that linguistic discrimination is having on linguistic diversity globally, partially using data from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and partially by offering a potential context for interpreting the emergence of practices that seek to reduce or modify speaker accents.

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