scholarly journals Analysis of Intervention Effectiveness Using Early Outbreak Transmission Dynamics to Guide Future Pandemic Management and Decision-Making in Kuwait

2021 ◽  
Author(s):  
Michael G. Tyshenko ◽  
Tamer Oraby ◽  
Joseph Craig Longenecker ◽  
Harri Vainio ◽  
Janvier Gasana ◽  
...  
Keyword(s):  
Decision Making ◽  
Reproduction Number ◽  
Control Measures ◽  
World Health ◽  
Transmission Model ◽  
Mobility Data ◽  
Imported Cases ◽  
Community Transmission ◽  
Health Organization ◽  
Case Data

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a World Health Organization designated pandemic that can result in severe symptoms and death that disproportionately affects older patients or those with comorbidities. Kuwait reported its first imported cases of COVID-19 on February 24, 2020. Analysis of data from the first three months of community transmission of the COVID-19 outbreak in Kuwait can provide important guidance for decision-making when dealing with future SARS-CoV-2 epidemic wave management. The analysis of intervention scenarios can help to evaluate the possible impacts of various outbreak control measures going forward which aim to reduce the effective reproduction number during the initial outbreak wave. Herein we use a modified susceptible-exposed-asymptomatic-infectious-removed (SEAIR) transmission model to estimate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait. We fit case data from the first 96 days in the model to estimate the basic reproduction number and used Google mobility data to refine community contact matrices. The SEAIR modelled scenarios allow for the analysis of various interventions to determine their effectiveness. The model can help inform future pandemic wave management, not only in Kuwait but for other countries as well.

scholarly journals COVID-19 outbreak and control in Kenya- Insights from a mathematical model

2021 ◽  
Author(s):  
Rachel Waema Mbogo ◽  
Titus Okello Orwa
Keyword(s):  
Reproduction Number ◽  
Control Measures ◽  
Capital City ◽  
World Health ◽  
Transmission Model ◽  
Global Public Health ◽  
The World ◽  
Rate Of Recovery ◽  
Health Organization ◽  
And Control

Abstract The coronavirus disease 2019 (COVID-19) pandemic reached Kenya in March 2020 with the initial cases reported in the capital city Nairobi and in the coastal area Mombasa. As reported by the World Health Organization, the outbreak of COVID-19 has spread across the world, killed many, collapsed economies and changed the way people live since it was first reported in Wuhan, China, in the end of 2019. As of May 25,2020 It had led to over 100,000 confirmed cases in Africa with over 3000 deaths. The trend poses a huge threat to global public health. 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.We employed a SEIHCRD mathematical transmission model with reported Kenyan data on cases of COVID-19 to estimate how transmission varies over time. The model is concise in structure, and successfully captures the course of the COVID-19 outbreak, and thus sheds light on understanding the trends of the outbreak. The next generation matrix approach was adopted to calculate the basic reproduction number (R0) from the model to assess the factors driving the infection . The results from the model analysis shows that non-pharmaceutical interventions over a relatively long period is needed to effectively get rid of the COVID-19 epidemic otherwise the rate of infection will continue to increase despite the increased rate of recovery.

scholarly journals COVID-19 outbreak and control in Kenya- Insights from a mathematical model

2020 ◽  
Author(s):  
Rachel Waema Mbogo ◽  
Titus Okello Orwa
Keyword(s):  
Reproduction Number ◽  
Control Measures ◽  
Capital City ◽  
World Health ◽  
Transmission Model ◽  
Global Public Health ◽  
The World ◽  
Rate Of Recovery ◽  
Health Organization ◽  
And Control

Abstract The coronavirus disease 2019 ( COVID -19) pandemic reached Kenya in March 2020 with the initial cases reported in the capital city Nairobi and in the coastal area Mombasa. As reported by the World Health Organization, the outbreak of COVID -19 has spread across the world, killed many, collapsed economies and changed the way people live since it was first reported in Wuhan, China, in the end of 2019. As of May 25,2020 It had led to over 100,000 confirmed cases in Africa with over 3000 deaths. The trend poses a huge threat to global public health. 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. We employed a SEIHCRD mathematical transmission model with reported Kenyan data on cases of COVID -19 to estimate how transmission varies over time. The model is concise in structure, and successfully captures the course of the COVID -19 outbreak, and thus sheds light on understanding the trends of the outbreak. The next generation matrix approach was adopted to calculate the basic reproduction number ( $R_0$ ) from the model to assess the factors driving the infection . The results from the model analysis shows that non-pharmaceutical interventions over a relatively long period is needed to effectively get rid of the COVID -19 epidemic otherwise the rate of infection will continue to increase despite the increased rate of recovery.

scholarly journals Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria

2020 ◽  
Vol 17 (9) ◽  
pp. 3054 ◽  
Author(s):  
Oyelola A. Adegboye ◽  
Adeshina I. Adekunle ◽  
Ezra Gayawan
Keyword(s):  
Doubling Time ◽  
Reproduction Number ◽  
Infected Individual ◽  
Credible Interval ◽  
World Health ◽  
Time Interval ◽  
Serial Interval ◽  
Imported Cases ◽  
Novel Coronavirus ◽  
Health Organization

On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05–0.10) with a doubling time of 9.84 days (95% CI: 7.28–15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65–8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83–7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26–1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

scholarly journals Modeling COVID-19 Transmissions and Evaluation of Large Scale Social Restriction in Jakarta, Indonesia

2020 ◽  
Author(s):  
A. Hasan ◽  
Y. Nasution ◽  
H. Susanto ◽  
E.R.M. Putri ◽  
V.R. Tjahjono ◽  
...  
Keyword(s):  
Quantitative Evaluation ◽  
Large Scale ◽  
Reproduction Number ◽  
World Health ◽  
Transmission Model ◽  
Time Varying ◽  
Short Term ◽  
Social Restriction ◽  
Short Term Forecasting ◽  
Health Organization

AbstractThis paper presents mathematical modeling and quantitative evaluation of Large Scale Social Restriction (LSSR) in Jakarta between 10 April and 4 June 2020. The special capital region of Jakarta is the only province among 34 provinces in Indonesia with an average Testing Positivity Rate (TPR) below 5% recommended by the World Health Organization (WHO). The transmission model is based on a discrete-time compartmental epidemiological model incorporating suspected cases. The quantitative evaluation is measured based on the estimation of the time-varying effective reproduction number (ℛt). Our results show the LSSR has been successfully suppressed the spread of COVID-19 in Jakarta, which was indicated by ℛt < 1. However, once the LSSR was relaxed, the effective reproduction number increased significantly. The model is further used for short-term forecasting to mitigate the course of the pandemic.

scholarly journals COVID-19 outbreak, social distancing and mass testing in Kenya - Insights from a mathematical model 

2020 ◽  
Author(s):  
Rachel Waema Mbogo ◽  
John W. Odhiambo
Keyword(s):  
Informal Settlement ◽  
Target Population ◽  
Control Measures ◽  
World Health ◽  
Transmission Model ◽  
Global Public Health ◽  
The World ◽  
The Government ◽  
Novel Coronavirus ◽  
Health Organization

Abstract As reported by the World Health Organization (WHO), the world is currently facing a devastating pandemic of a novel coronavirus ( COVID -19), which started as an outbreak of pneumonia of unknown cause in the Wuhan city of China in December 2019. Within days and weeks, the COVID -19 pandemic had spread to over 210 countries. By the end of April, COVID -19 had caused over three million confirmed cases of infections and 230,000 fatalities globally. The trend poses a huge threat to global public health. 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.We employed a SEIHCRD delay differential mathematical transmission model with reported Kenyan data on cases of COVID -19 to estimate how transmission varies over time and which population to target for mass testing. The model is concise in structure, and successfully captures the course of the COVID -19 outbreak, and thus sheds light on understanding the trends of the outbreak and the vulnerable populations. The results from the model gives insights to the government on the population to target for mass testing. The government should target population in the informal settlement for mass testing. People with pre-existing medical and non-medical conditions should be identified and given special medical care. With aggressive effective mass testing and adhering to the government directives and guidelines, we can get rid of COVID -19 epidemic.

scholarly journals COVID-19 outbreak, social distancing and mass testing in Kenya - Insights from a mathematical model 

2020 ◽  
Author(s):  
Rachel Waema Mbogo ◽  
John W. Oddhiambo
Keyword(s):  
Informal Settlement ◽  
Target Population ◽  
Control Measures ◽  
World Health ◽  
Transmission Model ◽  
Global Public Health ◽  
The World ◽  
The Government ◽  
Novel Coronavirus ◽  
Health Organization

Abstract As reported by the World Health Organization (WHO), the world is currently facing a devastating pandemic of a novel coronavirus ( COVID -19), which started as an outbreak of pneumonia of unknown cause in the Wuhan city of China in December 2019. Within days and weeks, the COVID -19 pandemic had spread to over 210 countries. By the end of April, COVID -19 had caused over three million confirmed cases of infections and 230,000 fatalities globally. The trend poses a huge threat to global public health. 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.We employed a SEIHCRD delay differential mathematical transmission model with reported Kenyan data on cases of COVID -19 to estimate how transmission varies over time and which population to target for mass testing. The model is concise in structure, and successfully captures the course of the COVID -19 outbreak, and thus sheds light on understanding the trends of the outbreak and the vulnerable populations. The results from the model gives insights to the government on the population to target for mass testing. The government should target population in the informal settlement for mass testing. People with pre-existing medical and non-medical conditions should be identified and given special medical care. With aggressive effective mass testing and adhering to the government directives and guidelines, we can get rid of COVID -19 epidemic.

Post COVID-19 Potentials and Innovation: The Future Supply Chain

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1054-1057
Author(s):  
Bindu Swetha Pasuluri ◽  
Anuradha S G ◽  
Manga J ◽  
Deepak Karanam
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Supply Chain ◽  
Supply Chains ◽  
World Health Organization ◽  
World Wide ◽  
World Health ◽  
Future Supply ◽  
Entire World ◽  
Health Organization

An unanticipated outburst of pneumonia of inexperienced in Wuhan, , China stated in December 2019. World health organization has recognized pathogen and termed it COVID-19. COVID-19 turned out to be a severe urgency in the entire world. The influence of this viral syndrome is now an intensifying concern. Covid-19 has changed our mutual calculus of ambiguity. It is more world-wide in possibility, more deeply , and much more difficult than any catastrophe that countries and organizations have ever faced. The next normal requires challenging ambiguity head-on and building it into decision-making. It is examined that every entity involved in running supply chains would require through major as employee, product, facility protocols, and transport would have to be in place. It is an urgent need of structuring to apply the lessons well-read for our supply chain setup. With higher managers now being aware of the intrinsic hazards in their supply chain, key and suggestions-recommendations will help to guide leader to commit to a newly planned, more consistent supply chain setup. Besides, the employees’ mental health is also a great concern.

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 Countries with delayed COVID-19 introduction – characteristics, drivers, gaps, and opportunities

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Zheng Li ◽  
Cynthia Jones ◽  
Girum S. Ejigu ◽  
Nisha George ◽  
Amanda L. Geller ◽  
...  
Keyword(s):  
Geographic Location ◽  
Control Measures ◽  
World Health ◽  
Small Island ◽  
Influenza Surveillance ◽  
Middle Income ◽  
School Closing ◽  
Travel Restrictions ◽  
Potential Factors ◽  
Health Organization

Abstract Background Three months after the first reported cases, COVID-19 had spread to nearly 90% of World Health Organization (WHO) member states and only 24 countries had not reported cases as of 30 March 2020. This analysis aimed to 1) assess characteristics, capability to detect and monitor COVID-19, and disease control measures in these 24 countries, 2) understand potential factors for the reported delayed COVID-19 introduction, and 3) identify gaps and opportunities for outbreak preparedness, particularly in low and middle-income countries (LMICs). We collected and analyzed publicly available information on country characteristics, COVID-19 testing, influenza surveillance, border measures, and preparedness activities in these countries. We also assessed the association between the temporal spread of COVID-19 in all countries with reported cases with globalization indicator and geographic location. Results Temporal spreading of COVID-19 was strongly associated with countries’ globalization indicator and geographic location. Most of the 24 countries with delayed COVID-19 introduction were LMICs; 88% were small island or landlocked developing countries. As of 30 March 2020, only 38% of these countries reported in-country COVID-19 testing capability, and 71% reported conducting influenza surveillance during the past year. All had implemented two or more border measures, (e.g., travel restrictions and border closures) and multiple preparedness activities (e.g., national preparedness plans and school closing). Conclusions Limited testing capacity suggests that most of the 24 delayed countries may have lacked the capability to detect and identify cases early through sentinel and case-based surveillance. Low global connectedness, geographic isolation, and border measures were common among these countries and may have contributed to the delayed introduction of COVID-19 into these countries. This paper contributes to identifying opportunities for pandemic preparedness, such as increasing disease detection, surveillance, and international collaborations. As the global situation continues to evolve, it is essential for countries to improve and prioritize their capacities to rapidly prevent, detect, and respond, not only for COVID-19, but also for future outbreaks.

scholarly journals The effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in different socioeconomic populations in Kuwait: a modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fatima Khadadah ◽  
Abdullah A. Al-Shammari ◽  
Ahmad Alhashemi ◽  
Dari Alhuwail ◽  
Bader Al-Saif ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Model ◽  
Socioeconomic Groups ◽  
Before And After ◽  
Cross Transmission ◽  
Community Transmission ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Abstract Background Aggressive non-pharmaceutical interventions (NPIs) may reduce transmission of SARS-CoV-2. The extent to which these interventions are successful in stopping the spread have not been characterized in countries with distinct socioeconomic groups. We compared the effects of a partial lockdown on disease transmission among Kuwaitis (P1) and non-Kuwaitis (P2) living in Kuwait. Methods We fit a modified metapopulation SEIR transmission model to reported cases stratified by two groups to estimate the impact of a partial lockdown on the effective reproduction number ($$ {\mathcal{R}}_e $$ R e ). We estimated the basic reproduction number ($$ {\mathcal{R}}_0 $$ R 0 ) for the transmission in each group and simulated the potential trajectories of an outbreak from the first recorded case of community transmission until 12 days after the partial lockdown. We estimated $$ {\mathcal{R}}_e $$ R e values of both groups before and after the partial curfew, simulated the effect of these values on the epidemic curves and explored a range of cross-transmission scenarios. Results We estimate $$ {\mathcal{R}}_e $$ R e at 1·08 (95% CI: 1·00–1·26) for P1 and 2·36 (2·03–2·71) for P2. On March 22nd, $$ {\mathcal{R}}_e $$ R e for P1 and P2 are estimated at 1·19 (1·04–1·34) and 1·75 (1·26–2·11) respectively. After the partial curfew had taken effect, $$ {\mathcal{R}}_e $$ R e for P1 dropped modestly to 1·05 (0·82–1·26) but almost doubled for P2 to 2·89 (2·30–3·70). Our simulated epidemic trajectories show that the partial curfew measure greatly reduced and delayed the height of the peak in P1, yet significantly elevated and hastened the peak in P2. Modest cross-transmission between P1 and P2 greatly elevated the height of the peak in P1 and brought it forward in time closer to the peak of P2. Conclusion Our results indicate and quantify how the same lockdown intervention can accentuate disease transmission in some subpopulations while potentially controlling it in others. Any such control may further become compromised in the presence of cross-transmission between subpopulations. Future interventions and policies need to be sensitive to socioeconomic and health disparities.

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