COVID-19 Transmission Patterns Research Articles

Data-driven analysis of the simulations of the spread of COVID-19 under different interventions of China

ABSTRACT Since February 2020, COVID-19 has spread rapidly to more than 200 countries in the world. During the pandemic, local governments in China have implemented different interventions to efficiently control the spread of the epidemic. Characterizing transmission of COVID-19 under some typical interventions is essential to help countries develop appropriate interventions. Based on the pre-symptomatic transmission patterns of COVID-19, we established a novel compartmental model: Susceptible-Infectious-Confirmed-Removed (SICR) model, which allowed the effective reproduction number to change over time, thus the effects of policies could be reasonably estimated. Using the epidemic data of Wuhan, Wenzhou, and Shenzhen, we migrated the corresponding estimated policy modes to South Korea, Italy, and the United States and simulated the potential outcomes for these countries when they adopted similar policy strategies to China. We found that the mild interventions implemented in Shenzhen were effective in controlling the epidemic in the early stage, while more stringent policies which were implemented in Wuhan and Wenzhou were necessary if the epidemic became severe and needed to be controlled in a short time.

A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load

COVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained mathcal{R}_{0} approx 1.50. Finally, an efficient numerical scheme of Adams–Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics.

Serological Profile of Children and Young Adults with at Least One SARS-CoV-2 Positive Cohabitant: An Observational Study.

At the end of 2019, a new disease caused by the novel coronavirus SARS-CoV-2 appeared in Wuhan Province in China. Children seemed to be infected less frequently than adults, and family clusters seemed to play an important role in the spread of the pandemic. The aim of this study is to evaluate the serological profile of children and young adults between 4 and 16 years of age in order to assess the transmission patterns of COVID-19 between cohabitants. The subjects lived with at least one cohabitant who tested positive for the disease using a nasopharyngeal swab. To avoid contact with the disease, families were interviewed by telephone. Forty-nine children and adolescents with a mean age of 11 years were then subjected to a rapid lateral flow chromatographic test. Of them, seven (14.3%) were immunoglobulin G (IgG)-positive, and four (8.2%) were immunoglobulin M (IgM)-positive. In total, 16.3% of the tested sample had antibodies against SARS-CoV-2: this may confirm the lower vulnerability of children to COVID-19, despite the small sample size. The time from the negativization of the cohabitant until the test day may have influenced the results, especially when this timeframe is wide.

Quarantine practices and COVID-19 transmission in a low-resource setting: Experience of Kerala, India.

Introduction:Disease transmission patterns of COVID-19 have shown that masking, social distancing, contact tracing and quarantine measures are important strategies for reducing transmission. The effective implementation of quarantine is determined by the commitment of the people and monitoring by the State. The aim of the study was to find out the effectiveness of home quarantine practises and its role in determining SARS CoV2 transmission.Methods:Record-based retrospective cohort study was conducted among expatriates of Kerala who were on quarantine at their homes and later tested positive for SARS –CoV-2. Quarantine practises were categorised as strict room quarantine, incomplete room quarantine, home quarantine and no quarantine. Risk of transmission was assessed using risk ratios. Multiple logistic regression analysis was performed to find out the determinants of SARS CoV2 transmission.Results:The median (IQR) age and duration of quarantine of 95 study participants were found to be 35 (29, 44) years and 7 (3,13) days, respectively. Majority of the participants practised strict room quarantine (57%), whereas 11.6%, 16.8% and 14.7% practiced incomplete room, home and no quarantine, respectively. Home quarantine without room quarantine had 24 times odds for transmitting disease [OR (95%CI)): 24.14 (4.87--119.75), P < 0.001] and not being in quarantine for any duration before being diagnosed was found to be 14 times riskier when compared with strict room quarantine [OR (95%CI)): 14.44 (2.42–86.17), P = 0.003].Discussion:Low-resource settings successful in the initial phases of COVID-19 pandemic should make periodic revisions in the quarantine guidelines while continually promoting physical distancing strategies.

Exploring the percentage of COVID-19 cases reported in the community in Canada and associated case fatality ratios.

While surveillance can identify changes in COVID-19 transmission patterns over time and space, sections of the population at risk, and the efficacy of public health measures, reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases. Our primary objective was to estimate the percentage of cases reported in the general community, considered as those that occurred outside of long-term care facilities (LTCFs), in specific provinces and Canada as a whole. We applied a methodology using the delay-adjusted case fatality ratio (CFR) to all cases and deaths, as well as those representing the general community. Our second objective was to assess whether the assumed CFR (mean = 1.38%) was appropriate for calculating underestimation of cases in Canada. Estimates were developed for the period from March 11th, 2020 to September 16th, 2020. Estimates of the percentage of cases reported (PrCR) and CFR varied spatially and temporally across Canada. For the majority of provinces, and for Canada as a whole, the PrCR increased through the early stages of the pandemic. The estimated PrCR in general community settings for all of Canada increased from 18.1% to 69.0% throughout the entire study period. Estimates were greater when considering only those data from outside of LTCFs. The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07% on March 11th, 2020 to 2.00% on September 16th, 2020. Therefore, the true CFR in the general community in Canada was likely less than 2% on September 16th. According to our analysis, some provinces, such as Alberta, Manitoba, Newfoundland and Labrador, Nova Scotia, and Saskatchewan reported a greater percentage of cases as of September 16th, compared to British Columbia, Ontario, and Québec. This could be due to differences in testing rates and criteria, demographics, socioeconomic factors, race, and access to healthcare among the provinces. Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study. The estimates provide context to the summative state of the pandemic in Canada, and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.

Limiting spread of COVID-19 in Ghana: Compliance audit of selected transportation stations in the Greater Accra region of Ghana.

Globally, little evidence exists on transmission patterns of COVID-19. Recommendations to prevent infection include appropriate and frequent handwashing plus physical and social distancing. We conducted an exploratory observational study to assess compliance with these recommendations in selected transportation stations in Ghana. A one-hour audit of 45 public transport stations in the Greater Accra region was carried out between 27th and 29th March 2020. Using an adapted World Health Organization (WHO) hand hygiene assessment scale, the availability and use of handwashing facilities, social distancing, and ongoing public education on COVID-19 prevention measures were assessed, weighted and scored to determine the level of compliance of stations. Compliance with recommendations was categorized as "inadequate" "basic", "intermediate" and "advanced", based on the overall score. Majority (80%) of stations in Accra have at least one Veronica Bucket with flowing water and soap, but the number of washing places at each station is not adequate. Only a small minority (18%) of stations were communicating the need to wash hands frequently and appropriately, and to practice social/physical distancing while at the station. In most stations (95%), hand washing practice was either not observed, or only infrequently. Almost all stations (93%) did not have alcohol-based hand sanitizers available for public use, while social distancing was rarely practiced (only 2%). In over 90% of the stations, face masks were either not worn or only worn by a few passengers. Compliance with COVID-19 prevention measures was inadequate in 13 stations, basic in 16 stations, intermediate in 7 stations, and advanced in 9 stations. Compliance with COVID-19 prevention measures in public transportation stations in the Greater Accra region remains a challenge. Awareness creation should aim to elevate COVID-19 risk perception of transportation operators and clients. Transport operators and stations need support and guidance to enforce hand washing and social distancing.

Effects of Temperature on Global Trends in Epidemiology of COVID-19

Identifying the effects of temperature on coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies all over the world. The aim of the study is to understand the effect of temperature on global epidemic trends, geographic distribution, and transmission patterns of COVID-19. Data on COVID-19 and temperature from 1st February 2020 to 30th April 2020 were included. The epidemic trends we analyzed using both descriptive and inferential statistics; the growth of affected countries was by descriptive analysis; and the global distribution and transmission trend by inferential statistics. Scattered plot diagram matrix was performed for Europe and Africa data. Based on 90 days COVID-19 affected and death data survival analysis was performed for South East Asian region. Lowest three months average temperature was in Europe (41.060F) from February 2020 to April 2020 (34.60F, 41.10F and 47.50F) shows the highest average confirmed case. Highest three months avg. temperature was Africa (74.40F) February 2020 to April 2020 (73.40F, 74.70F and 75.30F) shows the lowest avg. confirmed case and avg. death. In Europe, average temperatures with transmission positively correlated on the other hand in Africa average with transmission were negatively correlated. The results of the study suggested that, temperature had positive or negative effects on the geographical trend of COVID-19 confirmed cases, death cases, recovered case or transmission. It is being suggested that geographical regions with a lower temperature need to adopt the stricter control measures and WHO is a guideline to prevent rapid spread of COVID-19.

Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran

The COVID-19 has become a pandemic. The timing and nature of the COVID-19 pandemic response and control varied among the regions and from one country to the other, and their role in affecting the spread of the disease has been debated. The focus of this work is on the early phase of the disease when control measures can be most effective. We proposed a modified susceptible-exposed-infected-removed model (SEIR) model based on temporal moving windows to quantify COVID-19 transmission patterns and compare the temporal progress of disease spread in six representative regions worldwide: three Chinese regions (Zhejiang, Guangdong and Xinjiang) vs. three countries (South Korea, Italy and Iran). It was found that in the early phase of COVID-19 spread the disease follows a certain empirical law that is common in all regions considered. Simulations of the imposition of strong social distancing measures were used to evaluate the impact that these measures might have had on the duration and severity of COVID-19 outbreaks in the three countries. Measure-dependent transmission rates followed a modified normal distribution (empirical law) in the three Chinese regions. These rates responded quickly to the launch of the 1st-level Response to Major Public Health Emergency in each region, peaking after 1–2 days, reaching their inflection points after 10–19 days, and dropping to zero after 11–18 days since the 1st-level response was launched. By March 29th, the mortality rates were 0.08% (Zhejiang), 0.54% (Guangdong) and 3.95% (Xinjiang). Subsequent modeling simulations were based on the working assumption that similar infection transmission control measures were taken in South Korea as in Zhejiang on February 25th, in Italy as in Guangdong on February 25th, and in Iran as in Xinjiang on March 8th. The results showed that by June 15th the accumulated infection cases could have been reduced by 32.49% (South Korea), 98.16% (Italy) and 85.73% (Iran). The surface air temperature showed stronger association with transmission rate of COVID-19 than surface relative humidity. On the basis of these findings, disease control measures were shown to be particularly effective in flattening and shrinking the COVID-10 case curve, which could effectively reduce the severity of the disease and mitigate medical burden. The proposed empirical law and the SEIR-temporal moving window model can also be used to study infectious disease outbreaks worldwide.

Estimation of the probable outbreak size of novel coronavirus (COVID-19) in social gathering events and industrial activities

BackgroundThe reproduction number (R0) is vital in epidemiology to estimate the number of infected people and trace close contacts. R0 values vary depending on social activity and type of gathering events that induce infection transmissibility and its pathophysiology dependence. ObjectivesIn this study, we estimated the probable outbreak size of COVID-19 clusters mathematically using a simple model that can predict the number of COVID-19 cases as a function of time. MethodsWe proposed a mathematical model to estimate the R0 of COVID-19 in an outbreak occurring in both local and international clusters in light of published data. Different types of clusters (religious, wedding, and industrial activity) were selected based on reported events in different countries between February and April 2020. ResultsThe highest R0 values were found in wedding party events (5), followed by religious gathering events (2.5), while the lowest value was found in the industrial cluster (2). In return, this will enable us to assess the trend of coronavirus spread by comparing the model results and observed patterns. ConclusionsThis study provides predictive COVID-19 transmission patterns in different cluster types based on different R0 values. This model offers a contact-tracing task with the predicted number of cases, to decision-makers; this would help them in epidemiological investigations by knowing when to stop.

Using Test Positivity Rate (TPR) as an Indicator for Strategic Action in COVID-19: A Situational Analysis in Kerala, India

Administrators and policymakers have relied on test positivity rate (TPR) for making policy decisions regarding local, regional, and national lockdowns. It has the advantage of easily available data with an easy technique for calculation on day-to-day basis. However, concerns are being raised regarding its use as a sole indicator for determining movement restrictions and lockdowns. The present review provides a perspective of the alterations in TPR in Kasaragod district of Kerala during the first half of 2021. The variations in the number of antigen and reverse transcription polymerase chain reaction (rt-PCR) tests along with the trend of proportion of rt-PCR test are depicted. In places like Kerala where primary care system and contact tracing is comparatively robust than several other regions, testing the appropriate persons in a timely fashion alone is sufficient to cause an upswing in the TPR. Rather than daily change, the overall change in a larger time frame of 1 to 2 weeks could give early warning regarding the emergence of a new wave. TPR alone may not be able to reflect the transmission patterns of COVID-19. Using 7-day median value of TPR along with weekly tests done per 10,000 population, 7-day rolling average of active cases per 10,000 population, or daily number of new positive cases per 10,000 population could bring out a more composite indicator. Such an indicator reflecting the disease dynamics at regional levels will enable people to improve their livelihood without compromising on COVID-19.

What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization

BackgroundCOVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries. MethodsIn this work, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's population into seven age-groups: 0–6 years old (children); 7–14 (primary and junior high school students); 15–17 (high school students); 18–22 (university students); 23–44 (young/middle-aged people); 45–64 years old (middle-aged/elderly people); and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households; (2) schools, including primary/high schools as well as colleges and universities; (3) various physical workplaces; and (4) public places and communities where people can gather, such as stadiums, markets, squares, and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups in each of the four settings. By integrating the four contact matrices with the next-generation matrix, we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations. FindingsWe focus our study on 6 representative cities in China: Wuhan, the epicenter of COVID-19 in China, together with Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, which are five major cities from three key economic zones. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases). In Wuhan, the age-groups involving relatively intensive contacts in households and public/communities are dispersedly distributed. This can explain why the transmission of COVID-19 in the early stage mainly took place in public places and families in Wuhan. We estimate that Feb. 11, 2020 was the date with the highest transmission risk in Wuhan, which is consistent with the actual peak period of the reported case number (Feb. 4–14). Moreover, the surge in the number of new cases reported on Feb. 12 and 13 in Wuhan can readily be captured using our model, showing its ability in forecasting the potential/unconfirmed cases. We further estimate the disease transmission risks associated with different work resumption plans in these cities after the outbreak. The estimation results are consistent with the actual situations in the cities with relatively lenient policies, such as Beijing, and those with strict policies, such as Shenzhen. InterpretationWith an in-depth characterization of age-specific social contact-based transmission, the retrospective and prospective situations of the disease outbreak, including the past and future transmission risks, the effectiveness of different interventions, and the disease transmission risks of restoring normal social activities, are computationally analyzed and reasonably explained. The conclusions drawn from the study not only provide a comprehensive explanation of the underlying COVID-19 transmission patterns in China, but more importantly, offer the social contact-based risk analysis methods that can readily be applied to guide intervention planning and operational responses in other countries, so that the impact of COVID-19 pandemic can be strategically mitigated. FundingGeneral Research Fund of the Hong Kong Research Grants Council; Key Project Grants of the National Natural Science Foundation of China.

A Tempo-Geographic Analysis of Global COVID-19 Epidemic Outside of China

Background: Understanding the global epidemic trends, geographic distribution, and transmission patterns of COVID-19 contributes to providing timely information for the global response of the epidemic. This study aims to understand the global pandemic geo-spatial patterns and trends and identify new epicenters requiring urgent attention. Methods: Data on COVID-19 between 31st Dec. 2019 and 8th Mar. 2020 were included. The epidemic trend was analyzed using joinpoint regressions; the growth of affected countries was by descriptive analysis; and the global distribution and transmission trend by spatial analysis. Findings: The number of new cases in the regions outside of China slowly increased before 24th Feb. and rapidly accelerated after 24th Feb. Compared to China, other affected countries experienced a longer duration of a slow increase at the early stage and a rapid growth at the latter stages. The first apparent increase in the number of affected countries occurred from 23rd Jan to 1st Feb, where most of the first confirmed cases originated from China; the second apparent increase started from 25th Feb., where the majority originated from Italy and Iran. The geographic distribution changed from single-center (13th Jan. - 20th Feb.) to multi-centers pattern (20th Feb. – 14th Mar.) as South Korea, Italy, and Iran became epidemic centers. Interpretation: The joinpoint regression and geospatial analysis indicated a multi-center pandemic of COVID-19. Strategies to prevent the new multiple center as well as prevention ongoing transmission are needed. Funding Statement: This work was supported by the National Key Research and Development Program of China (2017YFE0103800), NIMH (R34MH119963), National Science and Technology Major Project (2018ZX10101-001-001-003), and the National Nature Science Foundation of China (81903371). Declaration of Interests: None. Ethics Approval Statement: Missing.