Phase Of Outbreak Research Articles

Media exposure and prevention behaviours in the emerging phase of a disease outbreak: a comparative study of two countries with different media systems

Media exposure and prevention behaviours in the emerging phase of a disease outbreak: a comparative study of two countries with different media systems

Mathematical Analysis of Four Fundamental Epidemiological Models for Monkeypox Disease Outbreaks: On the Pivotal Role of Human–Animal Order Parameters—In Memory of Hermann Haken

Four fundamental models that describe the spread of Monkeypox disease are analyzed: the SIR-SIR, SEIR-SIR, SIR-SEIR, and SEIR-SEIR models. They form the basis of most Monkeypox diseases models that are currently discussed in the literature. It is shown that the way the model subpopulations are organized in disease outbreaks and evolve relative to each other is determined by the relevant unstable system eigenvectors, also called order parameters. For all models, analytical expressions of the order parameters are derived. Under appropriate conditions these order parameters describe the initial outbreak phases of exponential increase in good approximation. It is shown that all four models exhibit maximally two order parameters and maximally one human–animal order parameter. The human–animal order parameter firmly connects the outbreak dynamics in the animal system with the dynamics in the human system. For the special case of the SIR-SIR model, it is found that the two possible order parameters completely describe the dynamics of infected humans and animals during entire infection waves. Finally, a simulation of a Monkeypox infection wave illustrates that in line with the aforementioned analytical results the leading order parameter explains most of the variance in the infection dynamics.

Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19.

Predicting the endemic/epidemic transition during the temporal evolution of a contagious disease. Indicators for detecting the transition endemic/epidemic, with four scalars to be compared, are calculated from the daily reported news cases: coefficient of variation, skewness, kurtosis, and entropy. The indicators selected are related to the shape of the empirical distribution of the new cases observed over 14 days. This duration has been chosen to smooth out the effect of weekends when fewer new cases are registered. For finding a forecasting variable, we have used the principal component analysis (PCA), whose first principal component (a linear combination of the selected indicators) explains a large part of the observed variance and can then be used as a predictor of the phenomenon studied (here the occurrence of an epidemic wave). A score has been built from the four proposed indicators using the PCA, which allows an acceptable level of forecasting performance by giving a realistic retro-predicted date for the rupture of the stationary endemic model corresponding to the entrance in the epidemic exponential growth phase. This score is applied to the retro-prediction of the limits of the different phases of the COVID-19 outbreak in successive endemic/epidemic transitions for three countries, France, India, and Japan. We provided a new forecasting method for predicting an epidemic wave occurring after an endemic phase for a contagious disease.

Impact of coronavirus disease 2019 on the utilization of hospital services and development of optimal pandemic control strategy in Chinese tertiary hospitals during the Omicron wave

BackgroundThis study aimed to assess the impact of coronavirus disease 2019 (COVID-19) on hospital service utilization and revenue in Chinese tertiary hospitals and develop an optimal pandemic control strategy (OPCS) for the peak period of the Omicron wave.MethodsRetrospective data from three Chinese tertiary hospitals (provincial, city, and county level) were analyzed for three phases: pre-outbreak (Jan-Apr 2019), outbreak (Jan-Apr 2020), and post-outbreak (Jan-Apr 2021). OPCS was developed under the guidance of the China government pandemic control policy during post-break phase of COVID-19. A decision-tree model was constructed to compare OPCS to strict pandemic control strategy during outbreak phase for the hospital service utilization and hospital revenue in a provincial tertiary hospital during the Omicron wave.ResultsOutpatient, emergency room (ER) visits, hospitalizations, and intensive care admissions dropped by 33.8–53.4% during the outbreak, with the provincial hospital being the most affected. Hospital revenue also declined, especially for the provincial hospital (40.1%). Post-outbreak, most services recovered, but ER visits remained lower (11.6% decrease for provincial hospital, 46.5% for county hospital). Total income and expenditure decreased, with the provincial hospital experiencing the most significant revenue reduction (45.7%). OPCS showed greater utilization of medical services (31.6 times more outpatient visits; 1.7 times more inpatient days; 3.4% more surgery volume) and higher revenue (¥220.8 million more) compared to the strict pandemic control strategy.ConclusionsCOVID-19 measures were associated with less hospital service utilization and revenue in Chinese tertiary hospitals. The developed OPCS in Chinese tertiary hospitals, focusing on isolating infected inpatients but not shutting down the hospital facilities exposed to virus, could be effective in optimizing hospital service utilization and hospital revenue during the Omicron wave.

The prevalence of contraceptive use among postpartum women and its associated factors during the early phase of COVID-19 outbreak: a time series study

BackgroundUnintended pregnancies can adversely affect maternal health, preventable through timely postpartum contraception. During the COVID-19 pandemic, family planning services were constrained by policies that curtailed outpatient visits. We investigated the prevalence of postpartum contraceptive initiation at King Chulalongkorn Memorial Hospital (KCMH) during January to June 2020, comparing with the same period in 2019, and identified factors associated with such initiation.MethodsWe reviewed the medical records of 4506 postpartum women who delivered at KCMH during the study period. Logistic regression was conducted to test the association between early COVID-19 phase deliveries and post-partum long acting reversible contraception (LARC) initiation including copper intrauterine devices, levonorgestrel intrauterine systems, contraceptive implants, and progestogen-only injectable contraceptives.ResultsA total of 3765 women (83.6%), of whom 1821 delivered during the pandemic and 1944 during the historical cohort period, were included in this study. The proportion of women who initiated non-permanent modern contraceptives at six weeks postpartum was comparable between the COVID-19 (73.4%) and historical cohort (75.3%) (p = 0.27) periods. The proportion of women who initiated LARC at six weeks postpartumwas comparable between the historical cohort period (22.5%) and the COVID-19 (19.7%) (p = 0.05) period. Accessing a six-week postpartum check-up was independently associated with LARC initiation, of which the adjusted odds ratio (OR) (95% confidence interval) was 3.01 (2.26 to 4.02).ConclusionsOur findings demonstrated that accessing postpartum care significantly associate with the use of LARC. The data suggest the strong influence of postpartum check-ups in facilitating the adoption of effective contraception, emphasizing the need for accessible postpartum care to sustain maternal health during health crises.

Level of Satisfaction in Higher Education L&T Based on VLE Functionalities: A COVID-19 Perspective

The problem of the sudden transition from offline to online due to COVID-19 was not easy and may involve significant challenges; faculty members are critical for the success of any university and their satisfaction does matter for online education. The purpose of this research was to determine the factors influencing the satisfaction of the faculty in the virtual learning environment and to focus on the responses of faculty members to the pandemic and their satisfaction levels due to challenges and benefits that faculty members faced during the initial phase of the COVID-19 outbreak. The six factors include flexibility, training, institutional factors, ease of use, technical factors and personal or psychological factors were utilised to understand the satisfaction levels of the instructors concerning the usage of the virtual instructional platforms. The hypothesis testing of the factors was conducted and different tests, that is, regression analysis, analysis of variance (ANOVA) and Cronbach alpha were utilised for analysing the data. Data were collected from 300 academic staff members from varied colleges and universities of Maharashtra and Madhya Pradesh. The results indicated that all the factors have a significant impact on the satisfaction levels of the academic staff members. Moreover, respondents opined about the challenges faced by the instructors in conducting online classes could be eased out via obtaining adequate training in batches from the academic institutions for the smooth conduct of the online sessions without any hindrances.

The Effectiveness of Digitalisation Usage on Customer Satisfaction Amidst the Covid-19 Outbreak and Recovery Phase

The utilisation of digital technologies to modify business models and generate fresh avenues for revenue and value creation has been acknowledged as a prominent factor. Consequently, it becomes evident that substantial transformation is imperative to effectively realise the ultimate objective of establishing digital enterprises. The issue of digitalization is predominantly an administrative concern rather than a technical one. This article focuses on the aspects that contribute to the enhancement of customer satisfaction in online shopping within China, during the Covid-19 pandemic and the subsequent recovery phase. Online buying has become a prevalent trend among consumers in recent times due to its inherent convenience. The researcher developed a conceptual framework in this study and intends to examine the correlation between seven variables: security, information availability, shipping, quality, pricing, time, and consumer satisfaction in the context of online purchasing. The framework has the potential to be utilized for future research and to make a valuable contribution to the existing body of information on emerging digital business models.

Spread of awareness of COVID-19 between December 2019 and March 2020 in France

During the early phase of outbreaks, awareness of the presence of the disease plays an important role in transmission dynamics. To investigate the processes of how people become aware of a disease, we conducted two complementary investigations. First, we surveyed 868 academic researchers in France, on the time and circumstances when they became aware of COVID-19 as an important event. We found that 25% did so before February 18th (first death in France), 50% did so before March 10th (first presidential allocution) and 75% did so before March 16th (announcement of the lockdown). Awareness came from nine categories of circumstances: (1) decisions taken by the government (elicited by 35.7% participants); (2) information from media or social media (24.7%); (3) conversation with friends (22.4%); (4) observed changes in their personal lives (14.0%); (5) decisions taken by the employer (9.2%); (6) observed changes at work (9.9%); (7) suspected case of COVID-19 in their entourage (3.1%); (8) fear for oneself or their entourage (2.8%) and (9) self-appraisal of scientific reports (2.8%). Second, we appraised three general media in France (a television news show, a radio news show, and a newspaper) and showed that COVID-19 became a preeminent topic only after March 1st 2020 when the epidemic is present on national soil. Our results show that multiple intricated factors prompt the awareness of an emerging infectious disease. Awareness is not solely driven by general media as they begin to focus on the topic late.

Embodied Uncertainty and COVID-19: Social identity, stigma, trust and coping through the pandemic

The COVID-19 pandemic has been a novel and evolving experience impacting health, livelihoods, well-being, decision-making, and community life. While uncertainty is seen as integral to the pandemic experience, limited literature has examined the consequences of the same, as embodied within individual and collective experiences. From the theoretical lens of Embodied Uncertainty (Sword-Daniels et al. 2018), the present study explored meaning making and coping with uncertainty as embodied in the lived experiences of the pandemic, through longitudinal qualitative research (LQR). Participants from a diverse socio demographic pool were interviewed in 2020 (N = 30) and 2021 (N = 14), following the two major phases of Covid-19 outbreak in India. Thematic analysis revealed complex intersections between social identity, stigma and economic strife and how novel coping strategies were being employed as the ongoing nature of the pandemic became a reality. We also found shifts in information seeking and institutional trust, shaped by the lived experiences of people as opposed to policy and state action. The results highlight key areas of vulnerability and coping with uncertainty in collective crises, gaps in state response and its impact, and how social identities shape interpretations of risks.

Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned

In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tools for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases (early, exponential growth, peak, and decline) during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.

Modeling COVID-19 Disease with Deterministic and Data-Driven Models Using Daily Empirical Data in the United Kingdom

The COVID-19 pandemic has had a significant impact on countries worldwide, including the United Kingdom (UK). The UK has faced numerous challenges, but its response, including the rapid vaccination campaign, has been noteworthy. While progress has been made, the study of the pandemic is important to enable us to properly prepare for future epidemics. Collaboration, vigilance, and continued adherence to public health measures will be crucial in navigating the path to recovery and building resilience for the future. In this article, we propose an overview of the COVID-19 situation in the UK using both mathematical (a nonlinear differential equation model) and statistical (time series modeling on a moving window) models on the transmission dynamics of the COVID-19 virus from the beginning of the pandemic up until July 2022. This is achieved by integrating a hybrid model and daily empirical case and death data from the UK. We partition this dataset into before and after vaccination started in the UK to understand the influence of vaccination on disease dynamics. We used the mathematical model to present some mathematical analyses and the calculation of the basic reproduction number (R0). Following the sensitivity analysis index, we deduce that an increase in the rate of vaccination will decrease R0. Also, the model was fitted to the data from the UK to validate the mathematical model with real data, and we used the data to calculate time-varying R0. The homotopy perturbation method (HPM) was used for the numerical simulation to demonstrate the dynamics of the disease with varying parameters and the importance of vaccination. Furthermore, we used statistical modeling to validate our model by performing principal component analysis (PCA) to predict the evolution of the spread of the COVID-19 outbreak in the UK on some statistical predictor indicators from time series modeling on a 14-day moving window for detecting which of these indicators capture the dynamics of the disease spread across the epidemic curve. The results of the PCA, the index of dispersion, the fitted mathematical model, and the mathematical model simulation are all in agreement with the dynamics of the disease in the UK before and after vaccination started. Conclusively, our approach has been able to capture the dynamics of the pandemic at different phases of the disease outbreak, and the result presented will be useful to understand the evolution of the disease in the UK and future and emerging epidemics.

GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models

Simple dynamic modeling tools can help generate real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. This tutorial-based primer introduces and illustrates GrowthPredict, a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to a broad audience, including students training in mathematical biology, applied statistics, and infectious disease modeling, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 1-parameter exponential growth model and the 2-parameter generalized-growth model, which have proven useful in characterizing and forecasting the ascending phase of epidemic outbreaks. It also includes the 2-parameter Gompertz model, the 3-parameter generalized logistic-growth model, and the 3-parameter Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks. We provide detailed guidance on forecasting time-series trajectories and available software (https://github.com/gchowell/forecasting_growthmodels), including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. This tutorial and toolbox can be broadly applied to characterizing and forecasting time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can help create forecasts to guide policy regarding implementing control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and the examples use publicly available data on the monkeypox (mpox) epidemic in the USA.

Bridging internalized HIV stigma and depressive symptoms among people living with HIV in China during the COVID-19 pandemic: a network analysis.

Previous research has documented that HIV-related stigma may be a significant trigger of mental health problems among people living with HIV (PLWH). However, less is known about how internalized HIV stigma is linked to depressive symptoms among PLWH during the COVID-19 pandemic. The current study sought to explore the network structure of internalized HIV stigma and depressive symptoms, along with bridge nodes, to elucidate how they co-exist. Participants were 1,197 Chinese PLWH (64.3% male, Mage = 41.52, SD = 9.20) who completed the measurements of internalized HIV stigma and depressive symptoms during the early phase of the COVID-19 outbreak (May 2020). Results revealed that "ashamed of having HIV" was identified as the most central nodes in the internalized HIV stigma network, whereas "mind wandered during tasks" ranked highest on centrality in the depressive symptoms network. Two bridge connections were exhibited within the combined internalized HIV stigma and depressive symptoms network model: "inferiority due to HIV" and "gloomy feelings" from internalized HIV stigma and depressive symptoms communities, respectively. This study is one of the first to examine the co-occurrence of internalized HIV stigma and depressive symptoms in the context of the COVID-19 pandemic using a network approach. These findings have potential clinical implications for mitigating depressive symptoms in populations facing socioeconomic disadvantage and vulnerability.

Short-Term Impact of Decomposing Crown-of-Thorn Starfish Blooms on Reef-Building Corals and Benthic Algae: A Laboratory Study

Outbreaks of crown-of-thorn starfish (COTS) have caused dramatic declines in reefs through predation on corals, but the post-bloom effects of COTS may still potentially threaten the environment and living organisms due to massive organic decomposition. This stimulation experiment showed that the decomposition of COTS debris triggered an extra mineralization process and resulted in acidifying, hypoxic, and eutrophic seawater. Consequently, the photosynthetic efficiency of coral symbionts decreased by 83%, and coral bleached after removing the stress within two days, then the coral skeleton dissolved at rates of 0.02–0.05 mg cm−2 day−1. Within two weeks, the photosynthesis and growth of benthic algae were suppressed by 27–86% and 1.5–16%, respectively. The mortality of turf algae and coralline algae indicated compromised primary productivity and limited coral recruitment, respectively. However, macroalgae, as coral competitors, became the only survivors, with increasing chlorophyll content. This study suggests a continuing decline of reefs during the collapse phase of COTS outbreaks and highlights the need for improving control strategies for the COTS population.

Patterns and impacts of an unprecedented outbreak of bark beetles in Central Europe: A glimpse into the future?

Natural disturbances have significantly intensified across European forests, with bark beetle outbreaks being the most rapidly escalating disturbance type. Since 2018, the Czech Republic (Central Europe) has become a Europe's disturbance epicentre due to the unprecedented outbreak of spruce bark beetle Ips typographus in the forests dominated by Norway spruce Picea abies. Here we provide novel insights into the impacts and dynamics of this disturbance from 2016 to 2022. The investigation is based on annual forest change maps developed by the classification of optical and Synthetic Aperture Radar satellite imagery. We identified seven major outbreak foci across the country, where the outbreaks culminated between 2018 and 2021. Most of the outbreak waves exhibited a symmetric shape, characterized by a three-year build-up phase, a single culmination year, and the subsequent decline. The substantial proportion of spruce remaining in the outbreak areas after the culmination point implies that resource depletion is an improbable cause for the outbreak's retreat. In the year of retreat, the proportion of spruce in the forest ranged between 26% and 36% in most of the outbreak areas. The disturbance dynamics manifested a transition from the emergence of new tree mortality spots in the early outbreak phase to their short-range expansion, suggesting density-dependent changes in bark beetle dispersal during the studied period. The core disturbance zone, pinpointed in 2022, covered an area of 9,000 ​km2 and experienced a 38% loss in forest cover. Within this area, forest fragmentation increased significantly, leading to a greater forest patch complexity and reduced connectivity among the patches. The presented findings can serve as a glimpse into the future for other European regions, revealing the potential impacts of natural disturbances amplified by climate change.

Metatranscriptomic insights into the microbial metabolic activities during an Ulva prolifera green tide in coastal Qingdao areas

Green tide, a typical marine environmental disaster that profoundly influenced the coastal areas, has been occurred consecutively in the South Yellow Sea of China since 2007. Herein, the active microbial community structure and metabolic pathways in Qingdao offshore during an Ulva prolifera green tide were investigated by using metatranscriptomic approach. The dominant active microbial taxa at the outbreak phase were primarily a functional group that can utilize organic matters derived from U. prolifera, such as Lentibacter, Polaribacter and Planktomarina. While the taxa involved in biogeochemical cycles, including Phaeobacter, Pseudomonas and Marinobacterium, dominated the active microbial communities at the decline phase. The expression level of enzymes involved in U. prolifera polysaccharides degradation was significantly higher at the outbreak phase compared to the decline phase. At the same time, the main players Glaciecola and Polarbacter showed similar trends, suggesting that the low competitiveness for nutrients of related microorganisms at this phase made them degrade more U. prolifera polysaccharides to meet their own nutrient needs, thereby accelerating the degradation of U. prolifera. According to KEGG annotation, the biogeochemical pathways including nitrogen cycle, sulfur cycle and methane oxidation altered during the green tide, with thiosulfate oxidation and methane oxidation probably being the crucial pathways at the outbreak and the decline phase respectively. The coupling of sulfide oxidation and denitrification was also observed in this study. Furthermore, the green tide in Qingdao offshore might impact the greenhouse effects induced by CH4 and N2O through influencing the related microbial processes.

Securitizing COVID-19 in an Environment of Low Political Trust

Securitization was a common practice of governments during the first phases of the COVID-19 outbreak. To successfully securitize a pandemic, a government has to convince its citizens of the magnitude of the threat. Trusted governments should be able to do this more effectively than untrusted ones. Hong Kong, our case study, is unique because the government managed to control the pandemic in a context of extremely low political trust. This paper examines the extent to which trust in the government and smart technologies influenced the securitization and management of the COVID-19 pandemic in Hong Kong. The results of this study suggest that under certain circumstances governments can successfully manage a health emergency even when they do not enjoy much political trust.

Anxiety and Depressive Symptoms among Discharged COVID-19 Patients from Isolation Centers in North-Central, Nigeria: A Cross-sectional Study

Abstract Introduction: The COVID-19 pandemic brought about an unprecedented number of people to be isolated and this negatively impacted on manifestations of various anxiety and depressive symptoms. Our study determined the prevalence of anxiety and depressive symptoms and the associated sociodemographic and clinical factors among discharged COVID-19 patients in North-Central, Nigeria. Methods: One hundred and sixteen discharged COVID-19 patients were assessed in a cross-sectional study during the early phase of the COVID-19 outbreak in Jos, North-Central, Nigeria. The age, gender, educational level, length of stay in isolation, and presence of symptoms of COVID-19 were assessed with a semi-structured questionnaire designed by the authors. The Hopkins Symptom Checklist-25 was used to assess for the symptoms of anxiety and depression among participants. Results: The mean age was 38.7 ± 14.6 years. More men (59%) were assessed with 67.2% of the participants “ever married,” and 43% had tertiary education. Voluntary admission was 62% and about 14% had symptoms of COVID-19. Forty-three percent had stayed for more than 10 days. The prevalence of symptoms of anxiety and depression was 17.2% and 9.5%, respectively, with 10.3% having combined symptoms. No significant association between the various sociodemographic and clinical factors, with symptoms of anxiety, except for the female gender which was significantly associated with depressive symptoms (P = 0.026). Conclusion: Isolation and COVID-19 impact on the manifestation of anxiety and depressive symptoms. Screening for anxiety and depressive symptoms should be integrated into the comprehensive psychological management of patients, especially for epidemic-prone diseases like COVID-19 that are associated with stigma.

An Investigation on the Ionospheric Response to the Volcanic Explosion at Hunga Ha'apai in 2021

AbstractThe Hunga Ha'apai volcano eruption (20.536°S, 175.382°W in Tonga), which started intermittently around December 2021 and most violently erupted on 15 January 2022, is considered to be the largest volcanic outbreak in recent decades. In this research, we derived the ionospheric total electron content (TEC) over Sanya (18.400°N, 109.600°E), Wuhan (30.530°N, 114.610°E), and Mohe (53.500°N, 122.370°E), from the Global Navigation Satellite System observations. Then we investigated the coupling between the volcano eruption and ionosphere through the TEC variations. The TEC anomaly decayed from about 10 days before main eruption of the Hunga Ha'apai volcano, and showed obvious fluctuations during the eruption phase. The TEC anomaly propagated periodically, with its autocorrelation‐analyzed period of about 16.5 hr during the intermittent outbreak and about 8 hr during the main outbreak phase. Its independently derived wavelet‐analyzed periods are about 9.4 hr during the intermittent outbreak and about 9.4 and 18.8 hr during the main outbreak phase. The propagation is mainly expressed in low frequencies, with energy concentrated in the range of 0–10−3 Hz. This study highlights that the preeruption activities may play an important role in the coupling between the volcanic eruption and ionosphere disturbances.

A compartmental model for COVID-19 to assess effects of non-pharmaceutical interventions with emphasis on contact-based quarantine

Relative to the number of casualties, COVID-19 ranks among the ten most devastating plagues in history. The pandemic hit the South Asian nation of Bangladesh in early March 2020 and has greatly impacted the socio-economic status of the country. In this article, we propose a compartmental model for COVID-19 dynamics, introducing a separate class for quarantined susceptibles, synonymous to isolation of individuals who have been exposed and are suspected of being infected. The current model assumes a perfect quarantine based on contact with infectious individuals. Numerical simulation is conducted to investigate the efficiency of disease control by segregating suspected individuals and other non-pharmaceutical interventions. In addition, we assort quantitatively the importance of parameters that influence the dynamics of the system. Fitting the system to the early phase of COVID-19 outbreaks in Bangladesh, by taking into account the cumulative number of cases with the data of the first 17-week period, the basic reproduction number is estimated as 1.69.