scholarly journals Real-time tracking and forecasting of the of COVID-19 outbreak in Kuwait: a mathematical modeling study

2020 ◽  
Author(s):  
Abdullah A. Al-Shammari ◽  
Hamad Ali ◽  
Barrak Al-Ahmad ◽  
Faisal H. Al-Refaei ◽  
Salman Al-Sabah ◽  
...  
Keyword(s):  
Public Health ◽  
Mathematical Modeling ◽  
Maximum Likelihood ◽  
Real Time ◽  
Reproduction Number ◽  
Control Measures ◽  
List Type ◽  
Imported Case ◽  
Real Time Tracking ◽  
Death Cases

AbstractKuwait has been experiencing a COVID-19 outbreak since the first imported case on Feb 24, 2020. Analysis of data from the early stage of COVID-19 outbreak in Kuwait can provide important information about the potential epidemic and healthcare burdens as well as assist in evaluating the potential impact of various outbreak control measures. Such control measures are essentially implemented to achieve a sufficient reduction in the effective reproduction number during an outbreak. In this study, we use a mathematical modeling framework to simulate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait and forecast the potential burden on the healthcare system. We calibrate the model against daily numbers of detected infection and death cases using a maximum likelihood framework and estimate both the basic and effective reproduction numbers. Our results indicate that the early control measures implemented in Kuwait had the effect of delaying the intensity of the outbreak but were unsuccessful in reducing Rt below 1. This highlights a need for a systematic investigation of the current public health interventions as well as a scientific surveillance tool that is sufficiently sensitive to outbreak temporal dynamics. Meanwhile, the developed model can serve as a public health tool to control the current outbreak and can be used to anticipate effective measures should a second wave re-emerge in Kuwait.HighlightsKuwait is experiencing a COVID-19 outbreak since the first imported case on Feb 24, 2020.We develop a mathematical model of disease transmission to provide a real-time tracking and forecasting tool for the epidemic outbreak in Kuwait as well as assess the potential epidemic and healthcare burdens and the effectiveness of early control measures.We calibrate the model against daily numbers of detected infection and death cases using a maximum likelihood framework.We find that early control measures had the effect of delaying and lowering the intensity of the outbreak but were unsuccessful in reducing the effective reproduction number below 1.

scholarly journals Is Nigeria really on top of COVID-19? Message from effective reproduction number

2020 ◽  
Author(s):  
Adeshina Israel Adekunle ◽  
Oyelola Adegboye ◽  
Ezra Gayawan ◽  
Emma McBryde
Keyword(s):  
Public Health ◽  
Compartmental Model ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Credible Interval ◽  
Control Measures ◽  
Maximum Value ◽  
Intervention Measures ◽  
Death Cases ◽  
Epidemiological Parameters

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.

scholarly journals Is Nigeria really on top of COVID-19? Message from effective reproduction number

2020 ◽  
Vol 148 ◽  
Author(s):  
A. I. Adekunle ◽  
O. A. Adegboye ◽  
E. Gayawan ◽  
E. S. McBryde
Keyword(s):  
Public Health ◽  
Compartmental Model ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Credible Interval ◽  
Control Measures ◽  
Maximum Value ◽  
Intervention Measures ◽  
Death Cases ◽  
Epidemiological Parameters

Abstract Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic 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 (R0) – this also enables us to estimate the initial daily transmission rate (β0). 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 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 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 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

Public Health Control Measures in Response to Global Pandemics and Drug Resistance

2015 ◽  
Vol 43 (S2) ◽  
pp. 49-56
Author(s):  
Polly J. Price
Keyword(s):  
Public Health ◽  
Communicable Disease ◽  
International Health ◽  
Control Measures ◽  
World Health ◽  
Legal Authority ◽  
List Type ◽  
Resistant Disease ◽  
Global Pandemic ◽  
Health Organization

These teaching materials explore the specific powers of governments to implement control measures in response to communicable disease, in two different contexts:The first context concerns global pandemic diseases. Relevant legal authority includes international law, World Health Organization governance and the International Health Regulations, and regulatory authority of nations.The second context is centered on U.S. law and concerns control measures for drug-resistant disease, using tuberculosis as an example. In both contexts, international and domestic, the point is to understand legal authority to address public health emergencies.

Salmonella investigations in ruminants

2017 ◽  
Vol 181 (14) ◽  
pp. 366-367 ◽  
Keyword(s):  
Public Health ◽  
Small Ruminant ◽  
Control Measures ◽  
List Type ◽  
Expert Group ◽  
Trigger Factors

Trigger factors for Salmonella infectionsSalmonella Typhimurium DT104 updateControl measures for Salmonella in livestockPublic health considerationsThese issues are considered in this month’s surveillance focus article, which has been prepared by Gareth Hateley, veterinary lead of the Cattle Expert Group, and Amanda Carson, veterinary lead of the Small Ruminant Expert Group, of the APHA Surveillance Intelligence Unit.

scholarly journals Family cluster of three cases of monkeypox imported from Nigeria to the United Kingdom, May 2021

2021 ◽  
Vol 26 (32) ◽  
Author(s):  
Gemma Hobson ◽  
James Adamson ◽  
Hugh Adler ◽  
Richard Firth ◽  
Susan Gould ◽  
...  
Keyword(s):  
Public Health ◽  
United Kingdom ◽  
Index Case ◽  
West African ◽  
Control Measures ◽  
Public Health Response ◽  
Imported Case ◽  
The United Kingdom ◽  
Health Response ◽  
The Family

Most reported cases of human monkeypox occur in Central and West Africa, where the causing virus is endemic. We describe the identification and public health response to an imported case of West African monkeypox from Nigeria to the United Kingdom (UK) in May 2021. Secondary transmission from the index case occurred within the family to another adult and a toddler. Concurrent COVID-19-related control measures upon arrival and at the hospital, facilitated detection and limited the number of potential contacts.

scholarly journals China’s effective control and other countries’ uncharted challenge against COVID-19: an epidemiological and modelling study

2020 ◽  
Author(s):  
Lingling Zheng ◽  
Qin Kang ◽  
Weiyao Liao ◽  
Xiujuan Chen ◽  
Shuai Huang ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Case Reports ◽  
Epidemiological Data ◽  
Control Measures ◽  
Time Dependent ◽  
Effective Control ◽  
List Type ◽  
Global Epidemic ◽  
Serial Interval

AbstractBackgroundOn the present trajectory, COVID is inevitably becoming a global epidemic, leading to concerns regarding the pandemic potential in China and other countries.ObjectiveIn this study, we use the time-dependent reproduction number (Rt) to comprise the COVID transmissibility across different countries.MethodsWe used data from Jan 20, 2019, to Feb 29, 2020, on the number of newly confirmed cases, obtained from the reports published by the CDC, to infer the incidence of infectious over time. A two-step procedure was used to estimate the Rt. The first step used data on known index-secondary cases pairs, from publicly available case reports, to estimate the serial interval distribution. The second step estimated the Rt jointly from the incidence data and the information data in the first step. Rt was then used to simulate the epidemics across all major cities in China and typical countries worldwide.ResultsBased on a total of 126 index-secondary cases pairs from 4 international regions, we estimated that the serial interval for SARS-2-CoV was 4.18 (IQR 1.92 – 6.65) days. Domestically, Rt of China, Hubei province, Wuhan had fallen below 1.0 on 9 Feb, 10 Feb and 13 Feb (Rt were 0.99±0.02, 0.99±0.02 and 0.96±0.02), respectively. Internationally, as of 26 Feb, statistically significant periods of COVID spread (Rt >1) were identified for most regions, except for Singapore (Rt was 0.92±0.17).ConclusionsThe epidemic in China has been well controlled, but the worldwide pandemic has not been well controlled. Worldwide preparedness and vulnerability against COVID-19 should be regarded with more care.What is already known on this subject?The basic reproduction number (R0) and the-time-dependent reproduction number (Rt) are two important indicators of infectious disease transmission. In addition, Rt as a derivative of R0 could be used to assess the epidemiological development of the disease and effectiveness of control measures. Most current researches used data from earlier periods in Wuhan and refer to the epidemiological features of SARS, which are possibly biased. Meanwhile, there are fewer studies discussed the Rt of COVID-19. Current clinical and epidemiological data are insufficient to help us understand the full view of the potential transmission of this disease.What this study adds?We use up-to-data observation of the serial interval and cases arising from local transmission to calculate the Rt in different outbreak level area and every province in China as well as five-top sever outbreak countries and other overseas. By comparing the Rt, we discussed the situation of outbreak around the world.

scholarly journals Real-Time Estimation of Rt for Supporting Public-Health Policies Against COVID-19

2020 ◽  
Vol 8 ◽  
Author(s):  
Sebastián Contreras ◽  
H. Andrés Villavicencio ◽  
David Medina-Ortiz ◽  
Claudia P. Saavedra ◽  
Álvaro Olivera-Nappa
Keyword(s):  
Public Health ◽  
Real Time ◽  
Reproduction Number ◽  
Case Reports ◽  
Mathematical Formulation ◽  
Time Estimation ◽  
Consensus Protocol ◽  
Viral Spread ◽  
Effective Interventions ◽  
Secondary Infections

In the absence of a consensus protocol to slow down the spread of SARS-CoV-2, policymakers need real-time indicators to support decisions in public health matters. The Effective Reproduction Number (Rt) represents the number of secondary infections generated per each case and can be dramatically modified by applying effective interventions. However, current methodologies to calculate Rt from data remain somewhat cumbersome, thus raising a barrier between its timely calculation and application by policymakers. In this work, we provide a simple mathematical formulation for obtaining the effective reproduction number in real-time using only and directly daily official case reports, obtained by modifying the equations describing the viral spread. We numerically explore the accuracy and limitations of the proposed methodology, which was demonstrated to provide accurate, timely, and intuitive results. We illustrate the use of our methodology to study the evolution of the pandemic in different iconic countries, and to assess the efficacy and promptness of different public health interventions.

scholarly journals The pandemic of social media panic travels faster than the COVID-19 outbreak

2020 ◽  
Vol 27 (3) ◽  
Author(s):  
Anneliese Depoux ◽  
Sam Martin ◽  
Emilie Karafillakis ◽  
Raman Preet ◽  
Annelies Wilder-Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Real Time ◽  
Control Measures ◽  
Public Health Officials ◽  
The Creation ◽  
And Control

We need to rapidly detect and respond to public rumours, perceptions, attitudes and behaviours around COVID-19 and control measures. The creation of an interactive platform and dashboard to provide real-time alerts of rumours and concerns about coronavirus spreading globally would enable public health officials and relevant stakeholders to respond rapidly with a proactive and engaging narrative that can mitigate misinformation.

scholarly journals COVID-19 outbreak in Mauritius: Logistic growth and SEIR modelling with quarantine and an effective reproduction number

2020 ◽  
Author(s):  
Antoine Gehin ◽  
Smita Goorah ◽  
Khemanand Moheeput ◽  
Satish Ramchurn
Keyword(s):  
Public Health ◽  
Growth Model ◽  
Reproduction Number ◽  
Control Measures ◽  
Logistic Growth ◽  
Cumulative Number ◽  
Public Health Response ◽  
The Public ◽  
Logistic Growth Model ◽  
Outbreak Size

SUMMARY Background and Objectives The island of Mauritius experienced a COVID-19 outbreak from mid-March to end April 2020. The first three cases were reported on March 18 (Day 1) and the last locally transmitted case occurred on April 26 (Day 40). An island confinement was imposed on March 20 followed by a sanitary curfew on March 23. Supermarkets were closed as from March 25 (Day 8). There were a total of 332 cases including 10 deaths from Day 1 to Day 41. Control of the outbreak depended heavily on contact tracing, testing, quarantine measures and the adoption of personal protective measures (PPMs) such as social distancing, the wearing of face masks and personal hygiene by Mauritius inhabitants. Our objectives were to model and understand the evolution of the Mauritius outbreak using mathematical analysis, a logistic growth model and an SEIR compartmental model with quarantine and a reverse sigmoid effective reproduction number and to relate the results to the public health control measures in Mauritius. Methods The daily reported cumulative number of cases in Mauritius were retrieved from the Worldometer website at https://www.worldometers.info/coronavirus/country/mauritius/. A susceptible-exposed-infectious-quarantined-removed (SEIQR) model was introduced and analytically integrated under the assumption that the daily incidence of infectious cases evolved as the derivative of the logistic growth function. The cumulative incidence data was fitted using a logistic growth model. The SEIQR model was integrated computationally with an effective reproduction number (R_e) varying in time according to a three-parameter reverse sigmoid model. Results were compared with the retrieved data and the parameters were optimised using the normalised root mean square error (NRMSE) as a comparative statistic. Findings A closed-form analytical solution was obtained for the time-dependence of the cumulative number of cases. For a small final outbreak size, the solution tends to a logistic growth. The cumulative number of cases was well described by the logistic growth model (NRMSE = 0.0276, R^2=0.9945) and by the SEIQR model (NRMSE = 0.0270, R^2=0.9952) with the optimal parameter values. The value of R_e on the day of the reopening of supermarkets (Day 16) was found to be approximately 1.6. Interpretation A mathematical basis has been obtained for using the logistic growth model to describe the time evolution of outbreaks with a small final outbreak size. The evolution of the outbreak in Mauritius was consistent with one modulated by a time-varying effective reproduction number resulting from the epidemic control measures implemented by Mauritius authorities and the PPMs adopted by Mauritius inhabitants. The value of R_e≈1.6 on the reopening of supermarkets on Day 16 was sufficient for the outbreak to grow to large-scale proportions in case the Mauritius population did not comply with the PPMs. However, the number of cases remained contained to a small number which is indicative of a significant contribution of the PPMs in the public health response to the COVID-19 outbreak in Mauritius.

scholarly journals Translating genomics into practice for real-time surveillance and response to carbapenemase-producing Enterobacteriaceae: evidence from a complex multi-institutional KPC outbreak

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4210 ◽  
Author(s):  
Jason C. Kwong ◽  
Courtney R. Lane ◽  
Finn Romanes ◽  
Anders Gonçalves da Silva ◽  
Marion Easton ◽  
...  
Keyword(s):  
Public Health ◽  
Infection Control ◽  
Real Time ◽  
The State ◽  
Control Measures ◽  
Reference Laboratory ◽  
Epidemiological Investigation ◽  
Transmission Networks ◽  
Healthcare Facilities ◽  
Incident Cases

BackgroundUntil recently,Klebsiella pneumoniaecarbapenemase (KPC)-producing Enterobacteriaceae were rarely identified in Australia. Following an increase in the number of incident cases across the state of Victoria, we undertook a real-time combined genomic and epidemiological investigation. The scope of this study included identifying risk factors and routes of transmission, and investigating the utility of genomics to enhance traditional field epidemiology for informing management of established widespread outbreaks.MethodsAll KPC-producing Enterobacteriaceae isolates referred to the state reference laboratory from 2012 onwards were included. Whole-genome sequencing was performed in parallel with a detailed descriptive epidemiological investigation of each case, using Illumina sequencing on each isolate. This was complemented with PacBio long-read sequencing on selected isolates to establish high-quality reference sequences and interrogate characteristics of KPC-encoding plasmids.ResultsInitial investigations indicated that the outbreak was widespread, with 86 KPC-producing Enterobacteriaceae isolates (K. pneumoniae92%) identified from 35 different locations across metropolitan and rural Victoria between 2012 and 2015. Initial combined analyses of the epidemiological and genomic data resolved the outbreak into distinct nosocomial transmission networks, and identified healthcare facilities at the epicentre of KPC transmission. New cases were assigned to transmission networks in real-time, allowing focussed infection control efforts. PacBio sequencing confirmed a secondary transmission network arising from inter-species plasmid transmission. Insights from Bayesian transmission inference and analyses of within-host diversity informed the development of state-wide public health and infection control guidelines, including interventions such as an intensive approach to screening contacts following new case detection to minimise unrecognised colonisation.ConclusionA real-time combined epidemiological and genomic investigation proved critical to identifying and defining multiple transmission networks of KPC Enterobacteriaceae, while data from either investigation alone were inconclusive. The investigation was fundamental to informing infection control measures in real-time and the development of state-wide public health guidelines on carbapenemase-producing Enterobacteriaceae surveillance and management.

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