A compartmental model for the analysis of SARS transmission patterns and outbreak control measures in China

Following the importation of Covid-19 into Nigeria on the 27 February 2020 and then the outbreak, the question is: how do we anticipate the progression of the ongoing epidemics following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of Covid-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R_0). This also enables us to estimate the daily transmission rate (β) that determines the effect of social distancing. We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on Covid-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 1.78. Most importantly, the R(t) is strictly greater than one from April 13 till 7 May 2020. The R_0 is estimated to be 2.42 with credible interval: (2.37, 2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below one. Also, the estimated fractional reported symptomatic cases are between 10 to 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

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Projected epidemiological consequences of the Omicron SARS-CoV-2 variant in England, December 2021 to April 2022

10.1101/2021.12.15.21267858 ◽

2021 ◽

Author(s):

Rosanna C Barnard ◽

Nicholas G Davies ◽

Carl A B Pearson ◽

Mark Jit ◽

W John Edmunds

Keyword(s):

Health Outcomes ◽

Compartmental Model ◽

Hospital Admissions ◽

Immune Escape ◽

Epidemiological Data ◽

Control Measures ◽

Gene Target ◽

S Gene ◽

Failure Data ◽

Pharmaceutical Interventions

The Omicron B.1.1.529 SARS-CoV-2 variant was first detected in late November 2021 and has since spread to multiple countries worldwide. We model the potential consequences of the Omicron variant on SARS-CoV-2 transmission and health outcomes in England between December 2021 and April 2022, using a deterministic compartmental model fitted to epidemiological data from March 2020 onwards. Because of uncertainty around the characteristics of Omicron, we explore scenarios varying the extent of Omicron's immune escape and the effectiveness of COVID-19 booster vaccinations against Omicron, assuming the level of Omicron's transmissibility relative to Delta to match the growth in observed S gene target failure data in England. We consider strategies for the re-introduction of control measures in response to projected surges in transmission, as well as scenarios varying the uptake and speed of COVID-19 booster vaccinations and the rate of Omicron's introduction into the population. These results suggest that Omicron has the potential to cause substantial surges in cases, hospital admissions and deaths in populations with high levels of immunity, including England. The reintroduction of additional non-pharmaceutical interventions may be required to prevent hospital admissions exceeding the levels seen in England during the previous peak in winter 2020-2021.

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Studies of COVID-19 Outbreak Control Using Agent-Based Modeling

Complex Systems ◽

10.25088/complexsystems.30.3.297 ◽

2021 ◽

Vol 30 (3) ◽

pp. 297-321

Author(s):

Shaoping Xiao ◽

◽

Ruicheng Liu ◽

Keyword(s):

Urban Communities ◽

Control Measure ◽

Agent Based Modeling ◽

Control Measures ◽

Published Data ◽

Agent Based ◽

Social Distancing ◽

Outbreak Control ◽

High Level ◽

General Community

An agent-based model was developed to study outbreaks and outbreak control for COVID-19, mainly in urban communities. Rules for people’s interactions and virus infectiousness were derived based on previous sociology studies and recently published data-driven analyses of COVID-19 epidemics. The calculated basic reproduction number of epidemics from the developed model coincided with reported values. There were three control measures considered in this paper: social distancing, self-quarantine and community quarantine. Each control measure was assessed individually at first. Later on, an artificial neural network was used to study the effects of different combinations of control measures. To help quantify the impacts of self-quarantine and community quarantine on outbreak control, both were scaled respectively. The results showed that self-quarantine was more effective than the others, but any individual control measure was ineffective in controlling outbreaks in urban communities. The results also showed that a high level of self-quarantine and general community quarantine, assisted with social distancing, would be recommended for outbreak control.

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What are the effects of COVID-19 outbreak control measures in long-term care facilities (LTCFs)?

Cochrane Clinical Answers ◽

10.1002/cca.3837 ◽

2021 ◽

Author(s):

Jane Burch ◽

Sera Tort

Keyword(s):

Long Term Care ◽

Control Measures ◽

Term Care ◽

Outbreak Control ◽

Care Facilities

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State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures

Scientific Reports ◽

10.1038/s41598-020-80044-3 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Shi Chen ◽

Qin Li ◽

Song Gao ◽

Yuhao Kang ◽

Xun Shi

Keyword(s):

United States ◽

New York ◽

Compartmental Model ◽

Spatial Interaction ◽

The United States ◽

State Policies ◽

Control Measures ◽

Specific Information ◽

Social Distancing ◽

Infected People

AbstractMost models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are most effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing.

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A Human-Pathogen SEIR-P Model for COVID-19 Outbreak under different intervention scenarios in Kenya

10.1101/2020.05.15.20102954 ◽

2020 ◽

Author(s):

Viona Nakhulo Ojiambo ◽

Mark Kimathi ◽

Samuel Mwalili ◽

Duncan Gathungu ◽

Rachel Mbogo

Keyword(s):

Compartmental Model ◽

Human Infection ◽

School Closure ◽

Control Measures ◽

Human Pathogen ◽

Movement Restriction ◽

Simulation Results ◽

Novel Coronavirus ◽

Fifth Order ◽

The Mathematical Model

This work describes the mathematical modelling and dynamics of a novel Coronavirus disease 2019 (COVID-19) in Kenya. The mathematical model assumes Human-Human infection as well as Human-Pathogen interaction. Using the SEIR (Susceptible-Exposed-Infected-Recovered) compartmental model with additional component of the pathogen,we simulated the dynamics of COVID-19 outbreak and impact of different control measures. The resulting system of ordinary differential equations (ODEs) are directly solved using a combination of fourth and fifth-order Runge-Kutta methods. Simulation results indicate that non-pharmaceutical measures such as school closure, social distancing and movement restriction emphatically flatten the epidemic peak curve hence leading to a smaller number of overall disease cases.

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Serial intervals observed in SARS-CoV-2 B.1.617.2 variant cases

10.1101/2021.06.04.21258205 ◽

2021 ◽

Author(s):

Rachael Pung ◽

Tze Minn Mak ◽

Adam J Kucharski ◽

Vernon J Lee ◽

Keyword(s):

Rapid Growth ◽

Reproduction Number ◽

Control Measures ◽

Confounding Factors ◽

Know How ◽

Outbreak Control ◽

Household Transmission ◽

Serial Interval

Rapid growth of the B.1.617.2 variant of SARS-CoV-2 has been observed in many countries. Broadly, the factors driving the recent rapid growth of COVID-19 cases could be attributed to shorten generation intervals or higher transmissibility (effective reproduction number, R), or both. As such, establishing reasons for the observed rapid growth will allow countries to know how best to enhance their outbreak control measures. In this study, we analysed the serial interval of household transmission pairs infected with SARS-CoV-2 B.1.617.2 variant and compared with those who were infected prior to the occurrence of the major global SARS-CoV-2 variants. After controlling for confounding factors, our findings suggest no significant changes in the serial intervals for SARS-CoV-2 cases infected with the B.1.617.2 variant. This in turn lends support for a hypothesis of a higher R for B.1.617.2 cases.

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Simulation of COVID-19 outbreaks via Graphical User Interface (GUI)

Journal of Public Health Research ◽

10.4081/jphr.2021.2130 ◽

2021 ◽

Author(s):

Norazaliza Mohd Jamil ◽

Norhayati Rosli ◽

Noryanti Muhammad

Keyword(s):

Compartmental Model ◽

Movement Control ◽

Pattern Search ◽

Control Measures ◽

Search Technique ◽

Unknown Parameters ◽

Intervention Measures ◽

Control Order ◽

Design And Methods ◽

Nelder Mead Algorithm

Background: This research aimed to model the outbreak of COVID-19 in Malaysia and develop a GUI-based model. Design and Methods: The model is an improvement of the susceptible, infected, recovery, and death (SIRD) compartmental model. The epidemiological parameters of the infection, recovery, and death rates were formulated as time dependent piecewise functions by incorporating the control measures of lockdown, social distancing, quarantine, lockdown lifting time and the percentage of people who abide by the rules. An improved SIRD model was solved via the 4th order Runge-Kutta (RK4) method and 14 unknown parameters were estimated by using Nelder-Mead algorithm and pattern-search technique. The publicly available data for COVID-19 outbreak in Malaysia was used to validate the performance of the model. The GUI-based SIRD model was developed to simulate the number of active cases of COVID-19 over time by considering movement control order (MCO) lifted date and the percentage of people who abide the rules. Results: The simulator showed that the improved SIRD model adequately fitted Malaysia COVID-19 data indicated by low values of root mean square error (RMSE) as compared to other existing models. The higher the percentage of people following the SOP, the lower the spread of disease. Another key point is that the later the lifting time after the lockdown, the lower the spread of disease. Conclusion: These findings highlight the importance of the society to obey the intervention measures in preventing the spread of the COVID-19 disease.

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