The impact of control strategies and behavioural changes on the elimination of Ebola from Lofa County, Liberia

Funk, Sebastian; Ciglenečki, Iza; Tiffany, Amanda; Gignoux, Etienne; Camacho, Anton; Eggo, Rosalind M; Aj, Kucharski; Wj, Edmunds; Bolongei, Josephus; Azuma, Phillip; Clement, Peter; Ts, Alpha; Sterk, Esther; Telfer, Barbara; Engel, Gregory; Parker, Lucy Anne; Suzuki, Motoi; Heijenberg, Nico; Reeder, Bruce

doi:10.1098/rstb.2016.0302

Cited by 81 publications

(77 citation statements)

References 36 publications

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“…Estimation of changes in transmission over time can provide insights into the epidemiological situation 3 and identify whether outbreak control measures are having a measurable effect. 4,5 Such analysis can inform predictions about potential future growth, 6 help estimate risk to other countries, 7 and guide the design of alternative interventions. 8 However, there are several challenges to such analyses, particularly in real time.…”

Section: Introductionmentioning

confidence: 99%

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Kucharski

Russell

Diamond

et al. 2020

The Lancet Infectious Diseases

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Background An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced. Methods We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) inWuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020.Findings We estimated that the median daily reproduction number (R t ) in Wuhan declined from 2·35 (95% CI 1·15-4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41-2·39) 1 week after. Based on our estimates of R t , assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.Interpretation Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually.

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Section: Introductionmentioning

confidence: 99%

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Kucharski

Russell

Diamond

et al. 2020

The Lancet Infectious Diseases

Self Cite

2,536

2,422

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“…In the presence of COVID19 outbreaks, it is crucial to understand the determinants of the transmission dynamics of this viral infectious disease for designing strategies to stop or reduce diffusion, empowering health policy with economic, social and environmental policies. This study focuses on statistical analyses of association between infected people and environmental, demographic and geographical factors that can explain transmission dynamics over time, and provide insights into the environmental situation to prevent and apply, a priori, appropriate control measures Funk et al, 2017;Riley et al, 2003). In particular, this study here can explain, whenever possible, factors determining the accelerated viral infectivity in specific regions to guide policymakers to prevent future epidemics similar to COVID-19 (Cooper et al, 2006;Kucharski et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics

Coccia

2020

Preprint

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What is COVID-19? Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death. What are the goals of this investigation? This study explains the geo-environmental determinants of the accelerated diffusion of COVID-19 in Italy that is generating a high level of deaths and suggests general lessons learned for a strategy to cope with future epidemics similar to COVID-19 to reduce viral infectivity and negative impacts in economic systems and society. What are the results of this study? The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society. What is a socioeconomic strategy to prevent future epidemics similar to COVID-19? Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19. This study must conclude that a strategy to prevent future epidemics similar to COVID 19 has also to be designed in environmental and sustainability science and not only in terms of biology.

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“…Some previous works model physical epidemics by adding parameters or compartments into the traditional SEIR and achieve good results. Funk et al [25] considered infectious of Ebola virus on the dead and split infectious people into those that seek healthcare and those that do not. Wang and Ruan [26] used limited data to simulate the SARS outbreak in Beijing by simplifying the model to a two-compartment suspect-probable model and a single-compartment probable model.…”

Section: Related Workmentioning

confidence: 99%

D2EA: Depict the Epidemic Picture of COVID-19

Liu

Zhao

Liu

et al. 2020

J. Shanghai Jiaotong Univ. (Sci.)

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The outbreak of coronavirus disease 2019 has aroused a global alert. To release social panic and guide future schedules, this article proposes a novel mathematical model, the Delay Differential Epidemic Analyzer (D 2 EA), to analyze the dynamics of epidemic and forecast its future trends. Based on the traditional Susceptible-Exposed-Infectious-Recovered (SEIR) model, the D 2 EA model innovatively introduces a set of quarantine states and applies both ordinary differential equations and delay differential equations to describe the transition between two states. Potential variations of practical factors are further considered to reveal the true epidemic picture. In the experiment part, we use the D 2 EA model to simulate the epidemic in Hubei Province. Fitting to the collected real data as non-linear optimization, the D 2 EA model forecasts that the accumulated confirmed infected cases in Hubei Province will reach the peak at the end of February and then steady down. We also evaluate the effectiveness of the quarantine measures and schedule the date to reopen Hubei Province.Nomenclature D(t)-Deaths of disease, person E(t)-Free exposed population (also infectious), person I(t)-Free infectious population, person N (t)-Total of free populations, person QE(t)-Quarantined exposed population, person QI (t)-Quarantined confirmed infected cases, person QN (t)-Quarantined suspected but negative cases, person QP (t)-Quarantined suspected and positive cases, person QS(t)-Quarantined healthy susceptible population, person r(t)-Number of contact with one person per unit time, person/d R(t)-Recovered population (also free), person S(t)-Free susceptible population, person t-Time, d T -Time needed to confirm one suspected case, d α-Death rate of QI (t), d −1 αI -Death rate of I(t), d −1 βE-Probability of transmission per contact with E(t) βI -Probability of transmission per contact with I(t) γ(t)-Recovery rate of QI (t), d −1 γE-Recovery rate of E(t), d −1 γI -Recovery rate of I(t), d −1 δ(t)-Quarantine rate of I(t), d −1 ε-Rate of progression from exposed state to infectious state, d −1 η-Ratio of common cold sufferers to all susceptibles τ -Quarantine duration, d

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The impact of control strategies and behavioural changes on the elimination of Ebola from Lofa County, Liberia

Cited by 81 publications

References 36 publications

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics

D2EA: Depict the Epidemic Picture of COVID-19

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