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

2017 ◽  
Vol 25 (03) ◽  
pp. 369-397 ◽  
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.

Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

2020 ◽  
Author(s):  
Lukman Olagoke ◽  
Ahmet E. Topcu
Keyword(s):  
Control Measure ◽  
Control Measures ◽  
World Health ◽  
Mitigation Measures ◽  
Effective Control ◽  
The World ◽  
Decision Making Processes ◽  
And Performance ◽  
Health Organization ◽  
The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

scholarly journals Spatial spread of the West Africa Ebola epidemic

2016 ◽  
Vol 3 (8) ◽  
pp. 160294 ◽  
Author(s):  
Andrew M. Kramer ◽  
J. Tomlin Pulliam ◽  
Laura W. Alexander ◽  
Andrew W. Park ◽  
Pejman Rohani ◽  
...  
Keyword(s):  
West Africa ◽  
Sierra Leone ◽  
Ebola Virus ◽  
Emerging Diseases ◽  
Relative Probability ◽  
Effective Response ◽  
Spatial Spread ◽  
Force Of Infection ◽  
International Border

Controlling Ebola outbreaks and planning an effective response to future emerging diseases are enhanced by understanding the role of geography in transmission. Here we show how epidemic expansion may be predicted by evaluating the relative probability of alternative epidemic paths. We compared multiple candidate models to characterize the spatial network over which the 2013–2015 West Africa epidemic of Ebola virus spread and estimate the effects of geographical covariates on transmission during peak spread. The best model was a generalized gravity model where the probability of transmission between locations depended on distance, population density and international border closures between Guinea, Liberia and Sierra Leone and neighbouring countries. This model out-performed alternative models based on diffusive spread, the force of infection, mobility estimated from cell phone records and other hypothesized patterns of spread. These findings highlight the importance of integrated geography to epidemic expansion and may contribute to identifying both the most vulnerable unaffected areas and locations of maximum intervention value.

Non-linear phenomena in host—parasite interactions

Parasitology ◽  
1989 ◽  
Vol 99 (S1) ◽  
pp. S59-S79 ◽  
Author(s):  
R. M. Anderson ◽  
R. M. May ◽  
S. Gupta
Keyword(s):  
Control Measure ◽  
Chemotherapeutic Agents ◽  
Control Measures ◽  
Developmental Cycle ◽  
Helminth Parasites ◽  
Control Effort ◽  
Host Parasite Interactions ◽  
Non Linear ◽  
Host Parasite ◽  
The Impact

SUMMARYThe paper examines non-linear dynamical phenomena in host—parasite interactions by reference to a series of different problems ranging from the impact on transmission of control measures based on vaccination and chemotherapy, to the effects of immunological responses targeted at different stages in a parasite's life-cycle. Throughout, simple mathematical models are employed to aid in interpretation. Analyses reveal that the influence of a defined control measure on the prevalence or intensity of infection, whether vaccination or drug treatment, is non-linearly related to the magnitude of control effort (as defined by the proportion of individuals vaccinated or treated with a drug). Consideration of the relative merits of gametocyte and sporozoite vaccines against malarial parasites suggests that very high levels of cohort immunization will be required to block transmission in endemic areas, with the former type of vaccine being more effective in reducing transmission for a defined level of coverage and the latter being better with respect to a reduction in morbidity. The inclusion of genetic elements in analyses of the transmission of helminth parasites reveals complex non-linear patterns of change in the abundance of different parasite genotypes under selection pressures imposed by either the host immunological defences or the application of chemotherapeutic agents. When resistance genes are present in parasite populations, the degree to which abundance can be suppressed by chemotherapy depends critically on the frequency and intensity of application, with intermediate values of the former being optimal. A more detailed consideration of the impact of immunological defences on parasite population growth within an individual host, by reference to the erythrocytic cycle of malaria, suggests that the effectiveness of a given immunological response is inversely related to the life-expectancy of the target stage in the parasite's developmental cycle.

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

2020 ◽  
Vol 117 (9) ◽  
pp. 5067-5073 ◽  
Author(s):  
Rebecca Kahn ◽  
Corey M. Peak ◽  
Juan Fernández-Gracia ◽  
Alexandra Hill ◽  
Amara Jambai ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Incubation Period ◽  
Population Connectivity ◽  
Disease Spread ◽  
Long Distance ◽  
Spatial Spread ◽  
Transmission Parameters ◽  
Incubation Periods ◽  
Using Data ◽  
The Impact

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.

scholarly journals Modelling respiratory infection control measure effects

2007 ◽  
Vol 136 (3) ◽  
pp. 299-308 ◽  
Author(s):  
C. M. LIAO ◽  
S. C. CHEN ◽  
C. F. CHANG
Keyword(s):  
Public Health ◽  
Respiratory Infections ◽  
Control Measure ◽  
Infection Control Measure ◽  
Control Measures ◽  
Integrated Analysis ◽  
Potential Control ◽  
Airborne Infection ◽  
Airborne Infections ◽  
The Impact

SUMMARYOne of the most pressing issues in facing emerging and re-emerging respiratory infections is how to bring them under control with current public health measures. Approaches such as the Wells–Riley equation, competing-risks model, and Von Foerster equation are used to prioritize control-measure efforts. Here we formulate how to integrate those three different types of functional relationship to construct easy-to-use and easy-to-interpret critical-control lines that help determine optimally the intervention strategies for containing airborne infections. We show that a combination of assigned effective public health interventions and enhanced engineering control measures would have a high probability for containing airborne infection. We suggest that integrated analysis to enhance modelling the impact of potential control measures against airborne infections presents an opportunity to assess risks and benefits. We demonstrate the approach with examples of optimal control measures to prioritize respiratory infections of severe acute respiratory syndrome (SARS), influenza, measles, and chickenpox.

A STUDY OF SCHISTOSOME TRANSMISSION DYNAMICS AND ITS CONTROL

2006 ◽  
Vol 14 (02) ◽  
pp. 295-302 ◽  
Author(s):  
PRASENJIT DAS ◽  
DEBASIS MUKHERJEE ◽  
A. K. SARKAR
Keyword(s):  
Numerical Simulations ◽  
Mathematical Analysis ◽  
Control Measure ◽  
Model Analysis ◽  
Unstable Equilibrium ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Delay Effect

This article concentrates on the study of delay effect on a model of schistosomiasis transmission with control measures such as predation or harvesting and chemotherapy. In the presence of predation or harvesting and chemotherapy, system admits multiple endemic equilibria. Mathematical analysis shows that they are opposite in nature regarding stability. One may observe switching phenomena for the unstable equilibrium by incorporating delay. The disease may be highly endemic if there is no control measure, which is obvious from the model analysis. Results obtained in this paper are also verified through numerical simulations.

scholarly journals Transmission dynamics of Ebola virus disease and intervention effectiveness in Sierra Leone

2016 ◽  
Vol 113 (16) ◽  
pp. 4488-4493 ◽  
Author(s):  
Li-Qun Fang ◽  
Yang Yang ◽  
Jia-Fu Jiang ◽  
Hong-Wu Yao ◽  
David Kargbo ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Ebola Virus ◽  
Virus Disease ◽  
Atmospheric Temperature ◽  
Transmission Dynamics ◽  
Ebola Virus Disease ◽  
Transmission Model ◽  
Household Transmission ◽  
Secondary Infections ◽  
The Impact

Sierra Leone is the most severely affected country by an unprecedented outbreak of Ebola virus disease (EVD) in West Africa. Although successfully contained, the transmission dynamics of EVD and the impact of interventions in the country remain unclear. We established a database of confirmed and suspected EVD cases from May 2014 to September 2015 in Sierra Leone and mapped the spatiotemporal distribution of cases at the chiefdom level. A Poisson transmission model revealed that the transmissibility at the chiefdom level, estimated as the average number of secondary infections caused by a patient per week, was reduced by 43% [95% confidence interval (CI): 30%, 52%] after October 2014, when the strategic plan of the United Nations Mission for Emergency Ebola Response was initiated, and by 65% (95% CI: 57%, 71%) after the end of December 2014, when 100% case isolation and safe burials were essentially achieved, both compared with before October 2014. Population density, proximity to Ebola treatment centers, cropland coverage, and atmospheric temperature were associated with EVD transmission. The household secondary attack rate (SAR) was estimated to be 0.059 (95% CI: 0.050, 0.070) for the overall outbreak. The household SAR was reduced by 82%, from 0.093 to 0.017, after the nationwide campaign to achieve 100% case isolation and safe burials had been conducted. This study provides a complete overview of the transmission dynamics of the 2014−2015 EVD outbreak in Sierra Leone at both chiefdom and household levels. The interventions implemented in Sierra Leone seem effective in containing the epidemic, particularly in interrupting household transmission.

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

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.

MODELING CHOLERA TRANSMISSION UNDER DISEASE CONTROL MEASURES

2020 ◽  
pp. 1-26
Author(s):  
MARGARET BROWN ◽  
MIKO JIANG ◽  
CHAYU YANG ◽  
JIN WANG
Keyword(s):  
Disease Control ◽  
Disease Transmission ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Threshold Dynamics ◽  
Education Programs ◽  
Multiple Control ◽  
Indirect Transmission ◽  
Transmission Pathways ◽  
The Impact

We present a new mathematical model to investigate the transmission dynamics of cholera under disease control measures that include education programs and water sanitation. The model incorporates the impact of education programs into the disease transmission rates and that of water sanitation into the environmental pathogen dynamics. We conduct a detailed analysis to the autonomous system of the model and establish the local and global stabilities of its equilibria that characterize the threshold dynamics of cholera. We then perform an optimal control study on the general model with time-dependent controls and explore effective approaches to implement the education programs and water sanitation while balancing their costs. Our analysis and simulation highlight the complex interaction among the direct and indirect transmission pathways of the disease, the intrinsic growth of the environmental pathogen and the impact of multiple control measures, and their roles in collectively shaping the transmission dynamics of cholera.

Grey relational evaluation of impact and control of malaria in Sub-Saharan Africa

2019 ◽  
Vol 9 (4) ◽  
pp. 415-431 ◽  
Author(s):  
Tawiah Kwatekwei Quartey-Papafio ◽  
Sifeng Liu ◽  
Sara Javed
Keyword(s):  
West Africa ◽  
Malaria Incidence ◽  
Control Measures ◽  
Sub Saharan Africa ◽  
Content Type ◽  
Independent States ◽  
Sub Saharan ◽  
And Control ◽  
Grey Relational ◽  
The Impact

Purpose The rise in malaria deaths discloses a decline of global malaria eradication that shows that control measures and fund distribution have missed its right of way. Therefore, the purpose of this paper is to study and evaluate the impact and control of malaria on the independent states of the Sub-Saharan African (SSA) region over the time period of 2010–2017 using Deng’s Grey incidence analysis, absolute degree GIA and second synthetic degree GIA model. Design/methodology/approach The purposive data sampling is a secondary data from World Developmental Indicators indicating the incidence of new malaria cases (per 1,000 population at risk) for 45 independent states in SSA. GIA models were applied on array sequences into a single relational grade for ranking to be obtained and analyzed to evaluate trend over a predicted period. Findings Grey relational analysis classifies West Africa as the highly infectious region of malaria incidence having Burkina Faso, Sierra Leone, Ghana, Benin, Liberia and Gambia suffering severely. Also, results indicate Southern Africa to be the least of all affected in the African belt that includes Eswatini, Namibia, Botswana, South Africa and Mozambique. But, predictions revealed that the infection rate is expected to fall in West Africa, whereas the least vulnerable countries will experience a rise in malaria incidence through to the next ten years. Therefore, this study draws the attention of all stakeholders and interest groups to adopt effective policies to fight malaria. Originality/value The study is a pioneer to unravel the most vulnerable countries in the SSA region as far as the incidence of new malaria cases is a concern through the use of second synthetic GIA model. The outcome of the study is substantial to direct research funds to control and eliminate malaria.

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