Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile’s incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

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Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

10.1101/2020.05.15.20103069 ◽

2020 ◽

Author(s):

Amna Tariq ◽

Eduardo A. Undurraga ◽

Carla Castillo Laborde ◽

Katia Vogt-Geisse ◽

Ruiyan Luo ◽

...

Keyword(s):

Low Income ◽

Exponential Growth ◽

Reproduction Number ◽

Growth Parameter ◽

Control Measures ◽

Transmission Dynamics ◽

Virus Spread ◽

Growth Trend ◽

Social Distancing ◽

First Case

Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513188 cases, including ~14302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile's incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96( 95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

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Early transmission dynamics and control of COVID-19 in a southern hemisphere setting: Lima-Peru, February 29th-March 30th, 2020

10.1101/2020.04.30.20077594 ◽

2020 ◽

Cited By ~ 4

Author(s):

César V. Munayco ◽

Amna Tariq ◽

Gabriela G Soto-Cabezas ◽

Mary F. Reyes ◽

Andree Valle ◽

...

Keyword(s):

Exponential Growth ◽

Linear Growth ◽

Great Majority ◽

Transmission Dynamics ◽

Growth Trend ◽

Social Distancing ◽

Transmission Potential ◽

First Case ◽

Dynamics And Control ◽

The Government

AbstractThe COVID-19 pandemic that emerged in Wuhan China rapidly spread around the world. The daily incidence trend has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil (28320) followed by Peru (11475) as of April 15th, 2020. Although Peru implemented social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to increase. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru.We estimate the transmission potential of COVID-19, R, during the early phase of the outbreak, from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place.Prior to the implementation of the social distancing measures in Lima, we estimated the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped stem the spread of the virus, with the nearly exponential growth trend shifting to an approximately linear growth trend after the national emergency declaration.The COVID-19 epidemic in Lima followed an early exponential growth trend, which slowed down and turned into an almost linear growth trend after broad scale social distancing interventions were put in place by the government.Peru COVID-19 working group

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Estimates of the ongoing need for social distancing and control measures post-“lockdown” from trajectories of COVID-19 cases and mortality

European Respiratory Journal ◽

10.1183/13993003.01483-2020 ◽

2020 ◽

Vol 56 (1) ◽

pp. 2001483 ◽

Cited By ~ 6

Author(s):

Mike Lonergan ◽

James D. Chalmers

Keyword(s):

Severe Acute Respiratory Syndrome ◽

Economic Activity ◽

Regression Models ◽

Reproduction Number ◽

Viral Transmission ◽

Control Measures ◽

Social Distancing ◽

And Control ◽

Doubling Times

By 21 May 2020, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) had caused more than 5 million cases of coronavirus 2019 (COVID-19) across more than 200 countries. Most countries with significant outbreaks have introduced social distancing or “lockdown” measures to reduce viral transmission. So the key question now is when, how and to what extent these measures can be lifted.Publicly available data on daily numbers of newly confirmed cases and mortality were used to fit regression models estimating trajectories, doubling times and the reproduction number (R0) of the disease, before and under the control measures. These data ran up to 21 May 2020, and were sufficient for analysis in 89 countries.The estimates of R0 before lockdown based on these data were broadly consistent with those previously published: between 2.0 and 3.7 in the countries with the largest number of cases available for analysis (USA, Italy, Spain, France and UK). There was little evidence to suggest that the restrictions had reduced R far below 1 in many places, with France having the most rapid reductions: R0 0.76 (95% CI 0.72–0.82) based on cases, and 0.77 (95% CI 0.73–0.80) based on mortality.Intermittent lockdown has been proposed as a means of controlling the outbreak while allowing periods of increased freedom and economic activity. These data suggest that few countries could have even 1 week per month unrestricted without seeing resurgence of the epidemic. Similarly, restoring 20% of the activity that has been prevented by the lockdowns looks difficult to reconcile with preventing the resurgence of the disease in most countries.

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Prediction of confinement effects on the number of Covid-19 outbreak in Algeria

Mathematical Modelling of Natural Phenomena ◽

10.1051/mmnp/2020028 ◽

2020 ◽

Vol 15 ◽

pp. 37 ◽

Cited By ~ 1

Author(s):

Ali Moussaoui ◽

Pierre Auger

Keyword(s):

Reproduction Number ◽

Control Measures ◽

Cumulative Number ◽

Time Data ◽

Final Size ◽

First Case ◽

Algerian Population ◽

Fast Change ◽

Real Time Data ◽

The Basic Reproduction Number

The first case of coronavirus disease 2019 (COVID-19) in Algeria was reported on 25 February 2020. Since then, it has progressed rapidly and the number of cases grow exponentially each day. In this article, we utilize SEIR modelling to forecast COVID-19 outbreak in Algeria under two scenarios by using the real-time data from March 01 to April 10, 2020. In the first scenario: no control measures are put into place, we estimate that the basic reproduction number for the epidemic in Algeria is 2.1, the number of new cases in Algeria will peak from around late May to early June and up to 82% of the Algerian population will likely contract the coronavirus. In the second scenario, at a certain date T, drastic control measures are taken, people are being advised to self-isolate or to quarantine and will be able to leave their homes only if necessary. We use SEIR model with fast change between fully protected and risky states. We prove that the final size of the epidemic depends strongly on the cumulative number of cases at the date when we implement intervention and on the fraction of the population in confinement. Our analysis shows that the longer we wait, the worse the situation will be and this very quickly produces.

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Transmission of SARS-CoV-2 in Georgia, USA, 2020

10.1101/2020.05.19.20107219 ◽

2020 ◽

Author(s):

Kamalich Muniz-Rodriguez ◽

Gerardo Chowell ◽

Jessica S. Schwind ◽

Randall Ford ◽

Sylvia K. Ofori ◽

...

Keyword(s):

Growth Phase ◽

Exponential Growth ◽

Growth Pattern ◽

Cumulative Incidence ◽

The State ◽

Control Measures ◽

Reproductive Number ◽

Initial Growth ◽

Social Distancing ◽

Dougherty County

ABSTRACTObjectiveSARS-CoV-2 has significantly impacted Georgia, USA including two major hotspots, Metro Atlanta and Dougherty County in southwestern Georgia. With government deliberations about relaxing social distancing measures, it is important to understand the trajectory of the epidemic in the state of Georgia.MethodsWe collected daily cumulative incidence of confirmed COVID-19 cases in Georgia. We estimated the reproductive number (Re) of the COVID-19 epidemic on April 18 and May 2 by characterizing the initial growth phase of the epidemic using the generalized-growth model.ResultsThe data presents a sub-exponential growth pattern in the cumulative incidence curves. On April 18, 2020, Re was estimated as 1.20 (95% CI: 1.10, 1.20) for the state of Georgia, 1.10 (95% CI: 1.00, 1.20) for Dougherty County, and 1.20 (95% CI: 1.10, 1.20) for Metro Atlanta. Extending our analysis to May 2, 2020, Re estimates decreased to 1.10 (95% CI: 1.10, 1.10) for the state of Georgia, 1.00 (95% CI: 1.00, 1.10) for Dougherty County, and 1.10 (95% CI: 1.10, 1.10) for Metro Atlanta.ConclusionsTransmission appeared to be decreasing after the implementation of social distancing measures. However, these results should be interpreted with caution when considering relaxing control measures due to low testing rates.

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Transmission dynamics, serial interval and epidemiology of COVID-19 diseases in Hong Kong under different control measures

Wellcome Open Research ◽

10.12688/wellcomeopenres.15896.2 ◽

2020 ◽

Vol 5 ◽

pp. 91

Author(s):

Yung-Wai Desmond Chan ◽

Stefan Flasche ◽

Tin-Long Terence Lam ◽

Mei-Hung Joanna Leung ◽

Miu-Ling Wong ◽

...

Keyword(s):

Hong Kong ◽

Population Level ◽

Credible Interval ◽

Control Measures ◽

Transmission Dynamics ◽

Local Data ◽

First Case ◽

Serial Interval ◽

Imported Cases ◽

Epidemiological Characteristics

Background: The outbreak of coronavirus disease 2019 (COVID-19) started in Wuhan, China in late December 2019, and subsequently became a pandemic. Hong Kong had implemented a series of control measures since January 2020, including enhanced surveillance, isolation and quarantine, border control and social distancing. Hong Kong recorded its first case on 23 January 2020, who was a visitor from Wuhan. We analysed the surveillance data of COVID-19 to understand the transmission dynamics and epidemiology in Hong Kong. Methods: We constructed the epidemic curve of daily COVID-19 incidence from 23 January to 6 April 2020 and estimated the time-varying reproduction number (Rt) with the R package EpiEstim, with serial interval computed from local data. We described the demographic and epidemiological characteristics of reported cases. We computed weekly incidence by age and residential district to understand the spatial and temporal transmission of the disease. Results: COVID-19 disease in Hong Kong was characterised with local cases and clusters detected after two waves of importations, first in late January (week 4 to 6) and the second one in early March (week 9 to 10). The Rt increased to approximately 2 95% credible interval (CI): 0.3-3.3) and approximately 1 (95%CI: 0.2-1.7), respectively, following these importations; it decreased to below 1 afterwards from weeks 11 to 13, which coincided with the implementation, modification and intensification of different control measures. Compared to local cases, imported cases were younger (mean age: 52 years among local cases vs 35 years among imported cases), had a lower proportion of underlying disease (9% vs 5%) and severe outcome (13% vs 5%). Cases were recorded in all districts but the incidence was highest in those in the Hong Kong Island region. Conclusions: Stringent and sustained public health measures at population level could contain the COVID-19 disease at a relatively low level.

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Modelling COVID-19 cases in Nigeria: Forecasts, uncertainties, projections and the link with weather

10.21203/rs.3.rs-41193/v1 ◽

2020 ◽

Author(s):

Adeyeri O.E. ◽

Oyekan K.S.A. ◽

Ige S.O. ◽

Akinbobola A. ◽

Okogbue E.C.

Keyword(s):

Correlation Coefficient ◽

Exponential Growth ◽

Reproduction Number ◽

Correlation Coefficients ◽

World Health ◽

Estimation Methods ◽

Maximum Temperature ◽

Religious Activities ◽

Epidemic Curve ◽

First Case

Abstract The World Health Organization (WHO) declared COVID-19 a global pandemic on 11 March 2020 due to its global spread. In Nigeria, the first case was documented on 27 February 2020. Since then, it has spread to most parts of the country. This study models, forecasts and projects COVID-19 incidence, cumulative incidence and death cases in Nigeria using six estimation methods i.e. the attack rate, maximum likelihood, exponential growth, Markov chain monte Carlo (MCMC), time-dependent and the sequential Bayesian approaches. A sensitivity analysis with respect to the mean generation time is used to quantify the associated reproduction number uncertainties. The relationship between the COVID-19 incidence and five meteorological variables are further assessed. The result shows that the highest incidences are recorded in days with either religious activities or market days while the weekday trend decreases towards the weekend. It is also established that COVID-19 incidence significantly increases with increasing sea level pressure (0.7 correlation coefficient) and significantly decreases with increasing maximum temperature (-0.3 correlation coefficient). Also, selecting an optimal period for reproduction number estimates reduces the variability between estimates. As an example, in the EG approach, the epidemic curve that optimally fits the exponential growth is between 1- and 53-time units with reproduction number estimate of 1.60 [1.58; 1.62] at 95% confidence interval. However, this optimal reproduction number estimate is different from the default reproduction number estimate. Using the MCMC approach, the correlation coefficients between the observed and forecasted incidence, cumulative death and cumulative confirmed cases are 0.66, 0.92 and 0.90 respectively. The projections till December shows values approaching 1,000,000, 120,000 and 3,000,000 respectively. Therefore, timely intervention and effective preventive measures are immediately needed to mitigate a full-scale epidemic in the country.

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TRANSMISSION DYNAMICS AND CONTROL STRATEGIES OF COVID-19 IN WUHAN, CHINA

Journal of Biological System ◽

10.1142/s0218339020500096 ◽

2020 ◽

Vol 28 (03) ◽

pp. 543-560 ◽

Cited By ~ 8

Author(s):

LIUYONG PANG ◽

SANHONG LIU ◽

XINAN ZHANG ◽

TIANHAI TIAN ◽

ZHONG ZHAO

Keyword(s):

Reproduction Number ◽

Control Strategies ◽

Public Awareness ◽

Control Measures ◽

Transmission Dynamics ◽

Published Data ◽

Model Parameters ◽

Strict Control ◽

Dynamics And Control ◽

Numerical Calculation Method

In December 2019, a novel coronavirus, SARS-COV-2, was identified among patients in Wuhan, China. Two strict control measures, i.e., putting Wuhan on lockdown and taking strict quarantine rule, were carried out to contain the spread of COVID-19. Based on the different control measures, we divided the transmission process of COVID-19 into three stages. An SEIHR model was established to describe the transmission dynamics and was applied to fit the published data on the confirmed cases of Wuhan city from December 31, 2019 to March 25, 2020 to deduce the time when the first patient with COVID-19 appeared. The basic reproduction number was estimated in the first stage to demonstrate the number of secondary infectious cases generated by an average infectious case in the absence of policy intervention. The effective reproduction numbers in second and third stages were estimated to evaluate the effects of the two strict control measures. In addition, sensitivity analysis of the reproduction number according to model parameters was executed to demonstrate the effect of the control measures for containing the spread of COVID-19. Finally, the numerical calculation method was applied to investigate the influence of the different control measures on the spread of COVID-19. The results indicated that following the strict quarantine rule was very effective, and reducing the effective contact rates and improving the diagnosis rate were crucial in reducing the effective reproduction number, and taking control measures as soon as possible can effectively contain a larger outbreak of COVID-19. But a bigger challenge for us to contain the spread of COVID-19 was the transmission from the asymptomatic carriers, which required to raising the public awareness of self-protection and keeping a good physical protection.

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Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States

Scientific Reports ◽

10.1038/s41598-020-77751-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Yixuan Pan ◽

Aref Darzi ◽

Aliakbar Kabiri ◽

Guangchen Zhao ◽

Weiyu Luo ◽

...

Keyword(s):

United States ◽

Real Time ◽

Human Mobility ◽

The United States ◽

Control Measures ◽

Social Distancing ◽

First Case ◽

Mobility Behaviour ◽

Novel Coronavirus ◽

Behaviour Changes

AbstractSince the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks.

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COVID-19 epidemic in Sri Lanka: A mathematical and computational modelling approach to control

10.1101/2020.04.21.20073734 ◽

2020 ◽

Author(s):

WPTM Wickramaarachchi ◽

SSN Perera ◽

S Jayasignhe

Keyword(s):

Sri Lanka ◽

Computational Modelling ◽

Control Measures ◽

Critical Parameters ◽

World Population ◽

Sri Lankan ◽

Social Distancing ◽

First Case ◽

Local Case ◽

The Government

AbstractThe ongoing COVID19 outbreak originated in the city of Wuhan, China has caused a significant damage to the world population and the global economy. It has claimed more than 50,000 lives worldwide and more than one million of people have been infected as of 04th April 2020.In Sri Lanka, the first case of COVI19 was reported late January 2020 was a Chinese national and the first local case was identified in the second week of March. Since then, the government of Sri Lanka introduced various sequential measures to improve social distancing such as closure of schools and education institutes, introducing work from home model to reduce the public gathering, introducing travel bans to international arrivals and more drastically, imposed island wide curfew expecting to minimize the burden of the disease to the Sri Lankan health system and the entire community. Currently, there are 159 cases with five fatalities and also reported that 24 patients are recovered and discharged from hospitals.In this study, we use the SEIR conceptual model and its modified version by decomposing infected patients into two classes; patients who show mild symptoms and patients who tend to face severe respiratory problems and are required to treat in intensive care units. We numerically simulate the models for about five months period considering three critical parameters of COVID transmission mainly in the Sri Lankan context; efficacy of control measures, rate of overseas imported cases and time to introduce social distancing measures by the respective authorities.

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