Effectiveness Of Containment Measures Research Articles

The Effect of Containment Measures during the Covid-19 Pandemic to Sedentary Behavior of Thai Adults: Evidence from Thailand's Surveillance on Physical Activity 2019-2020.

Measures to contain the spread of coronavirus disease 2019 (Covid-19) imposed by governments have undoubtedly impacted on preventing its spread but may have also produced longer periods of sedentary living across all segments of society. To examine this phenomenon, this study compared the sedentary behavior (SB) of Thai adults before and during the Covid-19 pandemic. The 2019 and 2020 datasets of Thailand’s Surveillance on Physical Activity (SPA) were employed. A total of 5379 (SPA2019) and 6531 (SPA202020) persons age 18–64 years who had access to the Internet were included in the analysis. Measures imposed to contain the spread of Covid-19 infection were significantly associated with lower opportunity of Thai adults for work-related physical movement, and that increased their SB, particularly with the shift from onsite to online working platforms. Cumulative SB increased from 824 (before the pandemic) to 875 min/day during the pandemic. The odds of accumulating >13 h/day of SB was highest among females, young adults, those who completed post-secondary education, unemployed or working in the non-agriculture sector, having a chronic disease/condition, residing in an urban area, and living in a ‘higher-risk’ pandemic zone. The insignificant association of physical activity (PA) and the Fit from Home (FFH) intervention in reducing SB during the pandemic suggests that PA is not directly associated with SB, and that the FFH intervention was insufficient to prevent SB.

An integrated epidemic modelling framework for the real-time forecast of COVID-19 outbreaks in current epicentres

Various studies have provided a wide variety of mathematical and statistical models for early epidemic prediction of the COVID-19 outbreaks in Mainland China and other epicentres worldwide. In this paper, we present an integrated modelling framework, which incorporates typical exponential growth models, dynamic systems of compartmental models and statistical approaches, to depict the trends of COVID-19 spreading in 33 most heavily suffering countries. The dynamic system of SIR-X plays the main role for estimation and prediction of the epidemic trajectories showing the effectiveness of containment measures, while the other modelling approaches help determine the infectious period and the basic reproduction number. The modelling framework has reproduced the subexponential scaling law in the growth of confirmed cases and adequate fitting of empirical time-series data has facilitated the efficient forecast of the peak in the case counts of asymptomatic or unidentified infected individuals, the plateau that indicates the saturation at the end of the epidemic growth, as well as the number of daily positive cases for an extended period.

Impact of Pandemic Nonpharmacological Public Health Interventions in Response to COVID-19 on Hospitalizations for Pneumonia in Children in Brazil

Background: The knowledge about the impact of the nonpharmacological measures to control the COVID-19 pandemic can give insight to ways in which they can also be applied for other respiratory diseases and therefore possibly help to reduce the burden of respiratory conditions in health systems. To assess the impact of containment measures of the COVID-19 pandemic on pneumonia hospitalizations in children from 0 to 14 years of age in Brazil.   Methods: Data from hospital admissions for pneumonia were obtained from the Department of Informatics of Brazilian Public Health System database in the period of 2015–2020 and analyzed by macro-regions and age groups. To evaluate the effect of containment measures, used in the pandemic, on the incidence of pneumonia, the absolute reduction (without - with pandemic containment measures) and relative reduction (without - with pandemic containment measures / without pandemic containment measures) were calculated by analyzing the subsets 2015-2019 vs 2020.   Findings: Comparing the subsets of April-August 2015-2019 vs April-August 2020, there was an expressive reduction in the average incidence of hospitalizations, with numbers ranging from -87% [IRR 0.12 (0.10 to 0.14)] for < 4 years, -79% [IRR 0.21 (0.07 to 0.57)] for 5-9 years, -73% [IRR 0.26 (0.05 to 1.21)] for 10-14 and -86% [IRR 0.14 (0.06 to 0.29)] for  <14 years. When comparing the subsets by macro-regions of Brazil (April-August 2015-2019 vs April-August 2020), a drop in hospitalizations was observed which ranged from -77% [IRR 0.23 (0.15 to 0.34)] in the Southeast to -87% [IRR 0.12 (0.07 to 0.21)] in the Northeast in children under 4 years. For children 5-9 years the fall ranged from -69% [IRR 0.30 (0.11 to 0.79)] in the Southeast region to -90% [IRR 0.10 (0.02 to 0.38)] in the South and in children 10-14 years, hospitalizations ranged from -73% [IRR 0.26 (0.05 to 1.21)] to -89% [IRR 0.19 (0.03 to 0.98)] in the South.   Interpretation: We found a significant decrease in cases of pneumonia during the COVID-19 pandemic. Nonpharmacological public health interventions can contribute to the decline of other respiratory infectious diseases. Funding: The present work was carried out with the support of the Coordination of Improvement of Personnel Higher Education - Brazil (CAPES) - Financing Code 001. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. Declaration of Interest: None to declare.

Spatiotemporal Analysis of COVID-19 Incidence Data.

(1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model’s parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings.

Prevention and Control of COVID-19 Pandemic on International Cruise Ships: The Legal Controversies.

During the COVID-19 pandemic in 2020, a number of international cruise ships were infected, thereby resulting in serious public health and human rights problems. Multiple difficulties were encountered in the prevention and control of the coronavirus disease onboard ships, while rule-based international cooperation in this regard appeared inefficient and ineffective. By applying interdisciplinary methodologies, including empirical research of law, policy science, and health studies, this research reviewed the legal difficulties in the prevention and control of COVID-19 on international cruise ships and sought solutions from a policy-making and strategic perspective. We found that, apart from the inherent nature of cruise ships such as crowded semi-enclosed areas, shared sanitary facilities and limited medical resources, there are also nonnegligible legal reasons affecting the effectiveness of containment measures on board. In particular, there is ambiguity and even inconsistency of relevant international norms and domestic regulations, and some of the key rules are neither mandatory nor enforceable. We conclude by suggesting that rule-based international cooperation on this issue must be strengthened with respect to information sharing and management, a more effective supervisory mechanism, clarification of key rules over jurisdiction and distributions of obligations among the port states, flag states, nationality states, and cruise ship companies.

Effects of the COVID-19 Pandemic on childhood immunization in Morocco: electronic survey of 103 pediatricians

Introductionnul ne peut nier les résultats protecteurs satisfaisants de la vaccination contre plusieurs maladies graves dans le monde et particulièrement au Maroc. L´extension de la pandémie COVID-19 dans notre pays a entrainé une baisse importante de la vaccination des enfants, ce qui pourrait causer des flambées épidémiques dans le futur. D´où l´intervention du ministère de la santé pour pallier à cette situation. L´objectif était de montrer l´ampleur du relâchement de la vaccination en temps COVID-19 et sortir avec des recommandations visant à rétablir ce changement.Méthodesil s´agit d´une étude transversale sur l´impact de cette pandémie sur le suivi vaccinal des enfants. Nous avons mené une enquête nationale auprès des pédiatres sous forme d´un questionnaire électronique via Google Forms. Nous avons procédé à la collecte, l´analyse et l´interprétation des résultats.Résultatscent trois pédiatres marocains ont répondu au questionnaire. Plus de 2 tiers des pédiatres exercent au secteur privé et pratiquent tout le calendrier vaccinal. La majorité des pédiatres (95%) ont reçu des questions émanant des craintes des parents sur la vaccination. Nous avons noté que 82,5% des parents hésitent à venir au cabinet et 5,8% refusent de faire la vaccination pendant cette période. Environ 22% des pédiatres ont arrêté totalement la vaccination et 72,8% ont décalé les vaccins d´un mois.Conclusionil est essentiel de maintenir la confiance de la population dans la vaccination. Une évaluation continue de la couverture vaccinale, des recommandations claires et une large sensibilisation du grand public seront nécessaire pour répondre aux changements vaccinaux durant la pandémie de COVID-19.

The Bayesian Susceptible-Exposed-Infected-Recovered model for the outbreak of COVID-19 on the Diamond Princess Cruise Ship

The outbreak of COVID-19 on the Diamond Princess Cruise Ship provides an unprecedented opportunity to estimate its original transmissibility with basic reproductive number (R0) and the effectiveness of containment measures. We developed an ordinary differential equation-based Susceptible-Exposed-Infected-Recovery (SEIR) model with Bayesian underpinning to estimate the main parameter of R0 determined by transmission coefficients, incubation period, and the recovery rate. Bayesian Markov Chain Monte Carlo (MCMC) estimation method was used to tackle the parameters of uncertainty resulting from the outbreak of COVID-19 given a small cohort of the cruise ship. The extended stratified SEIR model was also proposed to elucidate the heterogeneity of transmission route by the level of deck with passengers and crews. With the application of the overall model, R0 was estimated as high as 5.70 (95% credible interval: 4.23–7.79). The entire epidemic period without containment measurements was approximately 47 days and reached the peak one month later after the index case. The partial containment measure reduced 63% (95% credible interval: 60–66%) infected passengers. With the deck-specific SEIR model, the heterogeneity of R0 estimates by each deck was noted. The estimated R0 figures were 5.18 for passengers (5–14 deck), mainly from the within-deck transmission, and 2.46 for crews (2–4 deck), mainly from the between-deck transmission. Modelling the dynamic of COVID-19 on the cruise ship not only provides an insight into timely evacuation and early isolation and quarantine but also elucidates the relative contributions of different transmission modes on the cruise ship though the deck-stratified SEIR model.

K-SEIR-Sim: A simple customized software for simulating the spread of infectious diseases

Infectious disease is a great enemy of humankind. The ravages of COVID-19 are leading to profound crises across the world. There is an urgent requirement for analyzing the current pandemic situation, predicting trends over time, and assessing the effectiveness of containment measures. Thus, numerous statistical models, primarily based on the susceptible–exposed–infected–recovered or removed (SEIR) model, have been established. However, these models are highly technical, which are difficult for the public and governing bodies to understand and use. To address this issue, we developed a simple operating software based on our improved K-SEIR model termed as the kernel SEIR simulator (K-SEIR-Sim). This software includes natural propagation parameters, containment measure parameters, and certain characteristic parameters that can deduce the effects of natural propagation and containment measures. Further, the applicability of the proposed software was demonstrated using the examples of the COVID-19 outbreak in the United States, Wuhan city of China, Diamond Princess, and France. Operating results verified the potency of the proposed software in evaluating the epidemic situation and human intervention during COVID-19. Importantly, no installation is required and the software can perform real-time, backward-looking, and forward-looking analysis by functioning in data-driven and model-driven ways. All of them have considerable practical values in their applications according to the actual needs of personal use. Conclusively, K-SEIR-Sim is the first simple customized operating software that is highly valuable for the global fight against COVID-19 and other infectious diseases.

Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty.

After the introduction of drastic containment measures aimed at stopping the epidemic contagion from SARS-CoV2, many governments have adopted a strategy based on a periodic relaxation of such measures in the face of a severe economic crisis caused by lockdowns. Assessing the impact of such openings in relation to the risk of a resumption of the spread of the disease is an extremely difficult problem due to the many unknowns concerning the actual number of people infected, the actual reproduction number and infection fatality rate of the disease. In this work, starting from a SEIRD compartmental model with a social structure based on the age of individuals and stochastic inputs that account for data uncertainty, the effects of containment measures are introduced via an optimal control problem dependent on specific social activities, such as home, work, school, etc. Through a short time horizon approximation, we derive models with multiple feedback controls depending on social activities that allow us to assess the impact of selective relaxation of containment measures in the presence of uncertain data. After analyzing the effects of the various controls, results from different scenarios concerning the first wave of the epidemic in some major countries, including Germany, France, Italy, Spain, the United Kingdom and the United States, are presented and discussed. Specific contact patterns in the home, work, school and other locations have been considered for each country. Numerical simulations show that a careful strategy of progressive relaxation of containment measures, such as that adopted by some governments, may be able to keep the epidemic under control by restarting various productive activities.

Weather and Lockdown Compliance

The effectiveness of containment measures depends on both epidemiological and sociological mechanisms, most notably compliance with national lockdown rules. Yet, there is growing discontent with social distancing rules in many countries, which is expected to intensify further during summer. Using a highly granular dataset on compliance of over 105,000 individuals in the United Kingdom (UK), we find that compliance with lockdown policies tends to be high in the overall population, but that specific segments of society are substantially less compliant. Our findings show that warmer temperatures decrease non-compliance with governmental guidelines of individuals who are male, divorced, part-time employed, and/or parent of more than two children. Thus, as long as heard immunity through vaccination is not achieved and new strains demand containment measures to remain in place, understanding the individual determinants of non-compliance behaviour in different seasons of the year will remain important for policymakers to design effective policies in the future.

The Impact of Containment Measures and Monetary and Fiscal Responses on US Financial Markets During the COVID-19 Pandemic

This paper analyses the effects of containment measures and monetary and fiscal responses on US financial markets during the Covid-19 pandemic. More specifically, it applies fractional integration methods to analyse their impact on the daily S&P500, the US Treasury Bond Index (USTB), the S&P Green Bond Index (GREEN) and the Dow Jones (DJ) Islamic World Market Index (ISLAM) over the period 1/01/2020-10/03/2021. The results suggest that all four indices are highly persistent and exhibit orders of integration close to 1. A small degree of mean reversion is observed only for the S&P500 under the assumption of white noise errors and USTB with autocorrelated errors; therefore, market efficiency appears to hold in most cases. The mortality rate, surprisingly, seems to have affected stock and bond prices positively with autocorrelated errors. As for the policy responses, both the containment and fiscal measures had a rather limited impact, whilst there were significant announcement effects which lifted markets, especially in the case of monetary announcements. There is also evidence of a significant, positive response to changes in the effective Federal funds rate, which suggests that the financial industry, mainly benefiting from interest rises, plays a dominant role.

Impact of data accuracy on the evaluation of COVID-19 mitigation policies

AbstractEvaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number$ R(t) $, a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of$ R(t) $could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.

Stakeholders’ Perception of Xylella Fastidiosa (Xf) Disease Risk Assessment: First Results from Puglia (IT), Chania (GR), Valencia and Andalucia (ES)

Xylella fastidiosa (Xf) is an aerobic, Gram-negative bacterium of the monotypic genus Xylella. It is transmitted exclusively by xylem fluid feeding sap insects. Xf is one of the most dangerous plant bacteria worldwide, causing a variety of diseases, with huge impact for agriculture, and affecting cultivated plants of high economic value (e.g., olive trees, stone fruits—plums, almonds, cherries) or wide-spread ornamental plants (e.g., myrtle-leaf milkwort, oleander). In the frame of H2020 Project XF-ACTORS, analysis of the environmental, socio-economic and governance impact and vulnerability to Xf have been carried out in selected case study areas. To reach this goal, an extensive survey was conducted with the support of a comprehensive questionnaire in Puglia (Italy), Crete (Greece), Valencia and Andalusia (Spain). Questionnaires were completed through face-to-face interviews with local farmers, decision makers, extension experts and practitioners. The survey aimed to: 1) collect the point of view and perception of people about Xf disease and the containment plans; 2) identify possible gaps in communication, understand possible weak points in the communication strategy that could hamper the application and the effectiveness of containment measures; 3) understand the network of relationships existing among stakeholders in territorial contexts; and 4) collect suggestions from local people with a view to improving the management of information related to the disease. Questions were grouped under different main criteria: Knowledge, Perception, Practices, Involvement, Effectiveness, Responsibility. A scoring procedure assigned to each question a value to assess the corresponding indexes: DKI-Disease Knowledge Index, DPI-Disease Perception Index, FPI-Farm Practices Index, INV-Involvement Index, EFF-Effectiveness Index, RES-Responsibility Index. The overall values of RISK (DKI/DPI/FPI) and GOVERNANCE (INV/EFF/RES) were then obtained. To investigate linkages (correlation and causation) between Governance and Risk (perception and management) domains as well as among their indices, Structural Equation Modelling (SEM) technique was adopted. The proposed methodology proves to be useful to describe attitudes of respondents when facing the epidemic, as well as how they appreciate and tackle disease management. The work allows practical suggestions to be made to improve the knowledge-perception relationship that directly influences willingness to adopt preventive/control measures against the pathogen, and consequently will increase the efficiency of Xf disease management. The result of the whole analyses confirmed that the engagement of stakeholders, the involvement of expert groups (enlarged to experts in economy and social science) and an appropriate communication strategy are essential for a successful implementation of phytosanitary measures.

A hybrid deep learning and NLP based system to predict the spread of Covid-19 and unexpected side effects on people

The aim of this paper is to deeply analyze the unexpected side effects of people during the Covid-19 pandemic using the RNN based NLP sentiment analysis model. The normalized correlation values that is obtained by computing the cases values between the people behavior extracted and covid-19 reported case also has values close to 1 million by the end of June 2020 provided in dataset. In this research work, with more time, we would like to continue from the results we have achieved by training the RNN with NLP based sentiment analysis model for more extended periods of time for predicting the behavior of people during Covid-19 pandemic with 76.71% of accuracy which is high as compared to the CNN, such as days or weeks, in order to see how results can improve. The advancement in this field created an urge in me to research more on the techniques and methodologies developed for covid-19 extraction. During the outbreak of an epidemic, it is of immense interest to monitor the effects of containment measures and forecast of outbreak including epidemic peak affecting the behavior of people. To confront the change in behavior, a simple RNN based NLP sentiment analysis model is used to simulate the number of affected patients of Coronavirus disease. Our initial problem formulation involved investigating the ideal conditions and preprocessing for working with a specific NLP task: predicting the behavior during the specific time of May 20 – June 20 in 2020 for all four traits of common people during the Covid-19 pandemic.

A Mathematical and Statistical Estimation of Potential Transmission and Severity of COVID-19: A Combined Study of Romania and Pakistan.

During the outbreak of an epidemic, it is of immense interest to monitor the effects of containment measures and forecast of outbreak including epidemic peak. To confront the epidemic, a simple SIR model is used to simulate the number of affected patients of coronavirus disease in Romania and Pakistan. The model captures the growth in case onsets, and the estimated results are almost compatible with the actual reported cases. Through the calibration of parameters, forecast for the appearance of new cases in Romania and Pakistan is reported till the end of this year by analysing the current situation. The constant level of number of patients and time to reach this level is also reported through the simulations. The drastic condition is also discussed which may occur if all the preventive restraints are removed.

COVID-19 in Rajasthan: Status and Effects of Containment Measures

International Journal of Medicine and Public Health,2020,10,4,202-206.DOI:10.5530/ijmedph.2020.4.43Published:November 2020Type:Original ArticleCOVID-19 in Rajasthan: Status and Effects of Containment MeasuresRavi Prakash Sharma, Jyoti Gupta, Kusum Lata Gaur, Deepa Meena, Praveen Aswal, KK Sharma, Ruchi Singh, Raman Sharma, Bharti Malhotra, and Disha Meena Ravi Prakash Sharma1, Jyoti Gupta1, Kusum Lata Gaur2, Deepa Meena1,*, Praveen Aswal1, KK Sharma1, Ruchi Singh1, Raman Sharma2, Bharti Malhotra2, Disha Meena3 1Directorate of Medical and Health Services, Jaipur, Rajasthan, INDIA. 2Department of Community Medicine, SMS Medical College, Jaipur, Rajasthan, INDIA. 3Department of Microbiology, SMS Medical College Jaipur, Rajasthan, INDIA. 4Regional Office for Health and Family Welfare, Jaipur, Rajasthan, INDIA. Abstract:Background and Objectives: Presently world is facing Corona virus disease pandemic. This disease was first identified in Wuhan city of China which spread rapidly to more than 200 countries including India. Rajasthan is a state affected badly with this disease. So this present study was conducted to find out the status of COVID-19 in state and to find out the effects of containment measures taken. Materials and Methods: This record base case series type of study was conducted from reports received of confirmed COVID-19 cases and reported to the IDSP, DMHS from March 2nd 2020 to May 25th 2020, from various reporting units of Rajasthan. Desired information’s were collected, compiled and analyzed. Interpretation and Conclusion: There were 7351 laboratory-confirmed case including 4061 recovered cases and 166 deaths. Recovery rate and Case Fatality Rate were found 55.24% and 2.26% respectively. (2.4%) reported. Although patients of all ages were affected with this disease but majority (81.99%) were in 16-60 years of age group. In these positive cases male predominance was also observed with M:F ratio 2.11. Majority of district of Rajasthan were affected with this pandemic having about half of cases in three districts v.i.z. in Jaipur, Jodhpur and Udaipur districts. Average Doubling time has increased from 4 days to 18 days as a result of containment measures undertaken in Rajasthan. So these containment measures like social distancing, masking, washing hand frequently etc. Should be taken care of even after lockdown to prevent the COVID-19 infection. Keywords:Containment Measures, COVID-19, Effect of Lockdown, Lockdown, PHEIC, SARS CoV-2View:PDF (220.98 KB)

On the effectiveness of containment measures in controlling the COVID-19 pandemic: the role of labour market characteristics and governance

ABSTRACT This paper shows that countries where informality is commonplace, where a small share of jobs can be performed remotely, and where government effectiveness is low, experience smaller declines in COVID-19 cases after making containment measures more stringent. This appears to be related to the fact that in these countries mobility declines less after the implementation of containment policies, which facilitates the spread of the disease.

Effectiveness of Containment Measures Against COVID-19 in Singapore: Implications for Other National Containment Efforts.

We hypothesize that comprehensive surveillance of COVID-19 in Singapore has facilitated early case detection and prompt contact tracing and, with community-based measures, contained spread. We assessed the effectiveness of containment measures by estimating transmissibility (effective reproduction number, (Equation is included in full-text article.)) over the course of the outbreak. We used a Bayesian data augmentation framework to allocate infectors to infectees with no known infectors and determine serial interval distribution parameters via Markov chain Monte Carlo sampling. We fitted a smoothing spline to the number of secondary cases generated by each infector by respective onset dates to estimate (Equation is included in full-text article.)and evaluated increase in mean number of secondary cases per individual for each day's delay in starting isolation or quarantine. As of April 1, 2020, 1000 COVID-19 cases were reported in Singapore. We estimated a mean serial interval of 4.6 days [95% credible interval (CI) = 4.2, 5.1] with a SD of 3.5 days (95% CI = 3.1, 4.0). The posterior mean (Equation is included in full-text article.)was below one for most of the time, peaking at 1.1 (95% CI = 1.0, 1.3) on week 9 of 2020 due to a spreading event in one of the clusters. Eight hundred twenty-seven (82.7%) of cases infected less than one person on average. Over an interval of 7 days, the incremental mean number of cases generated per individual for each day's delay in starting isolation or quarantine was 0.03 cases (95% CI = 0.02, 0.05). We estimate that robust surveillance, active case detection, prompt contact tracing, and quarantine of close contacts kept (Equation is included in full-text article.)below one.

Validating a Phenomenological Mathematical Model for Public Health and Safety Interventions Influencing the Evolutionary Stages of Recent Outbreak for Long-Term and Short-Term Domains in Pakistan

During the outbreak of an epidemic, it becomes significantly essential to monitor the effects of containment measures and forecast the outbreak, including the epidemic peak. Many countries have either implemented strict lockdown to counter the spread of coronavirus disease or taken necessary preventive measures across the world to reduce the outbreak of this epidemic war. Several epidemic models have been presented across the world to examine the effects of public health-related strategies on mitigating the spread of current infectious disease, yet no reputable model has been presented for Pakistan as well as other South-Asian developing countries as per the authors’ knowledge. In this research, an actual coronavirus prediction in Pakistan is presented, which may guide the decision-makers as to how this pandemic has spread across the country and how it can be controlled. Furthermore, in the absence of targeted medicines, the analysis helps to develop a precise plan for the eradication of the outbreak by adopting the calculated steps at the right time. The mathematical phenomenological models have been adopted in this study to predict, project, and simulate the overall affected cases reflected due to the recent outbreak in Pakistan. These models predict the expected growth, and the estimated results are almost well matched with the real cases. Through the calibration of parameters and analyzing the current situation, forecast for the appearance of new cases in Pakistan is reported till the end of this year. The constant level of number of patients and time to reach specific levels are also reported through the simulations. The drastic conditions are also discussed which may occur if all the preventive restraints are removed. This research quantitatively describes the significant characteristics of the spread of corona cases. It acknowledges and provides an understanding of a short-term and long-term transmission of coronavirus outbreak in the country as three evolutionary phases. Therefore, this research provides a pathway to cope with the emerging threat of a severe outbreak in developing and nondeveloping countries.

Effectiveness of Containment Measures to Control the Spread of COVID-19 in North Africa.

Since the first case of severe acute respiratory syndrome coronavirus-2, identified in December 2019, in Wuhan in China, the number of cases rapidly increased into a pandemic. Governments worldwide have adopted different strategies and measures to interrupt the transmission of coronavirus disease 2019 (COVID-19). The main objective was to report and evaluate the effectiveness of the adopted measures in North Africa countries. In these countries, the effective reproductive number R(t), the naïve case fatality rate, and the adjusted case fatality rate were estimated and compared on different dates. The obtained results show that the early strict application of containment measures and confinement could help contain the spread of the epidemic and maintain the number of deaths low. These measures might be useful for other countries that are experiencing the start of local COVID-19 outbreaks. They could also serve to halt the spread of new epidemics or pandemics.