Transmissibility of COVID-19 and its association with temperature and humidity

Abstract Background: The new coronavirus disease COVID-19 outbroke in Wuhan, Hubei Province, China in December 2019, and has spread by human-to-human transmission to other areas. This study evaluated the transmissibility of the infectious disease and analyzed its association with temperature and humidity, in order to put forward suggestions on how to suppress the transmission. Methods: In this study, we revised the reported data in Wuhan to estimate the actual number of confirmed cases. Then we used the equation derived from the Susceptible–Exposed–Infectious–Recovered (SEIR) model to calculate R0 from January 24, 2020 to February 13, 2020 in 11 major cities in China for comparison. With the calculation results, we conducted correlation analysis and regression analysis between R0 and temperature and humidity to see the impact of weather on the transmissibility of COVID-19. Results: It was estimated that the cumulative number of confirmed cases had exceeded 45,000 by February 13, 2020 in Wuhan. The average R0 in Wuhan was 2.7011, significantly higher than those in other cities ranging from 1.7762 to 2.3700. The inflection points in the cities outside Hubei Province were between January 30, 2020 and February 3, 2020, while there had not been an obvious downward trend of R0 in Wuhan. R0 negatively correlated with both temperature and humidity, which was significant at the 0.01 level. Conclusions: The transmissibility of COVID-19 was strong and importance should be attached to the intervention of its transmission especially in Wuhan. According to the correlation between R0 and weather, the spread of disease will be suppressed as the weather warms.

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Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18041994 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1994

Author(s):

Lin-Yen Wang ◽

Tsair-Wei Chien ◽

Willy Chou

Keyword(s):

Item Response ◽

Theory Model ◽

Forest Plot ◽

Cumulative Number ◽

Observation Study ◽

Irt Model ◽

Inflection Points ◽

The Difference ◽

The Impact ◽

Absolute Advantage

Coronavirus disease 2019 (COVID-19) occurred in Wuhan and rapidly spread around the world. Assessing the impact of COVID-19 is the first and foremost concern. The inflection point (IP) and the corresponding cumulative number of infected cases (CNICs) are the two viewpoints that should be jointly considered to differentiate the impact of struggling to fight against COVID-19 (SACOVID). The CNIC data were downloaded from the GitHub website on 23 November 2020. The item response theory model (IRT) was proposed to draw the ogive curve for every province/metropolitan city/area in China. The ipcase-index was determined by multiplying the IP days with the corresponding CNICs. The IRT model was parameterized, and the IP days were determined using the absolute advantage coefficient (AAC). The difference in SACOVID was compared using a forest plot. In the observation study, the top three regions hit severely by COVID-19 were Hong Kong, Shanghai, and Hubei, with IPcase indices of 1744, 723, and 698, respectively, and the top three areas with the most aberrant patterns were Yunnan, Sichuan, and Tianjin, with IP days of 5, 51, and 119, respectively. The difference in IP days was determined (χ2 = 5065666, df = 32, p < 0.001) among areas in China. The IRT model with the AAC is recommended to determine the IP days during the COVID-19 pandemic.

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Impact of non-pharmaceutical interventions on the control of COVID-19 in Iran: A mathematical modeling study

10.21203/rs.3.rs-126532/v1 ◽

2020 ◽

Author(s):

Mehran Nakhaeizadeh ◽

Sana Eybpoosh ◽

Yunes Jahani ◽

Milad Ahmadi Gohari ◽

Ali Akbar Haghdoost ◽

...

Keyword(s):

Mathematical Modeling ◽

Mathematical Model ◽

Reproductive Number ◽

Cumulative Number ◽

Seir Model ◽

Control Programs ◽

East Region ◽

The Impact ◽

Middle East Region ◽

Pharmaceutical Interventions

Abstract Background During the first months of the COVID-19 pandemic, Iran reported high numbers of infections and deaths in the Middle East region. In the following months, the burden of this infection decreased significantly, possibly due to the impact of a package of interventions. We modeled the dynamics of COVID-19 infection in Iran to quantify the impacts of these interventions. Methods We used a modified susceptible–exposed–infected–recovered (SEIR) model to model the COVID-19 epidemic in Iran, from 21 January to 21 September 2020, using Markov chain Monte Carlo simulation to calculate 95% uncertainty intervals (UI). We used the model to assess the effectiveness of physical distancing measures and self-isolation under different scenarios. We also estimated the control reproductive number (Rc), using our mathematical model and epidemiologic data. Results If no non-pharmaceutical interventions (NPIs) were applied, there could have been a cumulative number of 51,800,000 (95% UI: 19,100,000–77,600,000) COVID-19 infections and 266,000 (95% UI: 119,000–476,000) deaths by September 21 2020. If physical distancing interventions, such as school/border closures and self-isolation interventions, had been introduced a week earlier than they were actually launched, a 30% reduction in the number of infections and deaths could have been achieved by September 21 2020. The observed daily number of deaths showed that the Rc was one or more than one almost every day during the analysis period. Conclusions Our models suggest that the NPIs implemented in Iran between 21 January and 21 September 2020 had significant effects on the spread of the COVID-19 epidemic. Therefore, we recommend that these interventions are considered when designing future control programs, while simultaneously considering innovative approaches that can minimize harmful economic impacts on the community and the state. Our study also showed that the timely implementation of NPIs showed a profound effect on further reductions in the numbers of infections and deaths. This highlights the importance of forecasting and early detection of future waves of infection and of the need for effective preparedness and response capabilities.

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An Improved SEIR Model Considering the Impact of Contact-Restricting Policies: A Case Study of COVID-19 in Wuhan

10.21203/rs.3.rs-715229/v1 ◽

2021 ◽

Author(s):

Silu Chen ◽

Jiangping Chen ◽

Tianyou Cheng

Keyword(s):

Infectious Disease ◽

Human Movement ◽

Development Stage ◽

Population Activity ◽

Substantial Impact ◽

Seir Model ◽

Prediction And Prevention ◽

The Government ◽

Travel Decisions ◽

The Impact

Abstract Dynamic modeling of infectious disease can simulate transmission processes of COVID-19, a newly been found infectious respiratory disease that has a substantial impact on both people's health and social development, and therefore plays an important role in the prediction and prevention of epidemics. Although there are many models that can accurately represent the number of infected patients, the influence of human factors on the transmission of the virus has not been fully investigated. Here, by considering the influence of policies on restricting contact between people, we modified the SEIR infectious disease model and developed a new model called the Quarantine-considering SEIR model (hereafter referred to as Q-SEIR), combining with dynamic parameter, contact rate, obtained by machine learning method, we can represent the effects of human movement and contact behavior during the epidemic. The experimental results show that this method can effectively represent the effect of patterns of population activity on the development of the epidemic. On one hand, our research results provide guidance for the government before issuing measures to restrict the movement and socialization of people; and on the other hand, our findings help identify the development stage of the epidemic more clearly for the public as well as provide information for citizens’ travel decisions.

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Modeling the Impact of Lock-down on COVID-19 Spread in Malaysia

10.1101/2020.07.17.208371 ◽

2020 ◽

Author(s):

Altahir A. Altahir ◽

Nirbhay Mathur ◽

Loshini Thiruchelvam ◽

Ghulam E. Mustafa Abro ◽

Syaimaa’ S. M. Radzi ◽

...

Keyword(s):

Infectious Disease ◽

Reproduction Number ◽

Formal Analysis ◽

Time Frame ◽

Immature Stage ◽

Epidemic Spread ◽

Prevention Measures ◽

Seir Model ◽

The World ◽

The Impact

AbstractAfter a breakdown notified in Wuhan, China in December 2019, COVID-19 is declared as pandemic diseases. To the date more than 13 million confirmed cases and more than half a million are dead around the world. This virus also attached Malaysia in its immature stage where 8718 cases were confirmed and 122 were declared as death. Malaysia responsibly controlled the spread by enforcing MCO. Hence, it is required to visualize the pattern of Covid-19 spread. Also, it is necessary to estimate the impact of the enforced prevention measures. In this paper, an infectious disease dynamic modeling (SEIR) is used to estimate the epidemic spread in Malaysia. The main assumption is to update the reproduction number Rt with respect to the implemented prevention measures. For a time-frame of five month, the Rt was assumed to vary between 2.9 and 0.3. Moreover, the manuscript includes two possible scenarios: the first will be the extension of the stricter measures all over the country, and the second will be the gradual lift of the lock-down. After implementing several stages of lock-down we have found that the estimated values of the Rt with respect to the strictness degree varies between 0.2 to 1.1. A continuous strict lock-down may reduce the Rt to 0.2 and accordingly the estimated active cases will be reduced to 20 by the beginning of September 2020. In contrast, the second scenario considers a gradual lift of the enforced prevention measures by the end of June 2020, here we have considered three possible outcomes according to the MCO relaxation. Thus, the estimated values of Rt = 0.7, 0.9, 1.1, which shows a rapid increase in the number of active cases. The implemented SEIR model shows a close resemblance with the actual data recorded from 10, March till 7, July 2020.Author summaryConceptualization, A.A.A; methodology, A.A.A, N.M; validation, A.A.A, N.M; formal analysis, A.A.A; investigation, N.M, A.A.A; resources, G.E.M.A, L.T; data collection, L.T, N.M; writing—original draft preparation, A.A.A, L.T, G.E.M.A, N.M; writing—review and editing, V.S.A, S.C.D, B.S.G, P.S, S.A.B.M.Z, N.M; visualization, N.M; supervision, V.S.A; project administration, V.S.A. All authors have read and agreed to the published version of the manuscript

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Moving Average Based Index for Judging the Peak of the COVID-19 Epidemic

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17155288 ◽

2020 ◽

Vol 17 (15) ◽

pp. 5288 ◽

Cited By ~ 1

Author(s):

Yunting He ◽

Xiaojin Wang ◽

Hao He ◽

Jing Zhai ◽

Bingshun Wang

Keyword(s):

Infectious Disease ◽

Hong Kong ◽

Moving Average ◽

Hubei Province ◽

Emerging Infectious Disease ◽

Evaluation Index ◽

Normal Life ◽

The World ◽

Novel Coronavirus ◽

Reported Data

A pneumonia outbreak caused by a novel coronavirus (COVID-19) has spread around the world. A total of 2,314,621 laboratory-confirmed cases, including 157,847 deaths (6.8%) were reported globally by 20 April 2020. Common symptoms of COVID-19 pneumonia include fever, fatigue, and dry cough. Faced with such a sudden outbreak of emerging infectious disease, traditional models for predicting the peak of the epidemic often show inconsistent results. With the aim to timely judge the epidemic peak and provide support for decisions for resuming production and returning to normal life based on publicly reported data, we used a seven-day moving average of log-transformed daily new cases (LMA) to establish a new index named the “epidemic evaluation index” (EEI). We used SARS epidemic data from Hong Kong to verify the practicability of the new index, and then applied it to the COVID-19 epidemic analysis. The results showed that the epidemic peaked, respectively, on 9 February and 5 February 2020, in Hubei Province and other provinces in China. The proposed index can be applied for judging the epidemic peak. While the global COVID-19 epidemic reached its peak in the middle of April, the epidemic peaks in some countries have not yet appeared. Global and united efforts are still needed to eventually eliminate the epidemic.

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COVID-19 in India: State-wise Analysis and Prediction

10.1101/2020.04.24.20077792 ◽

2020 ◽

Cited By ~ 9

Author(s):

Palash Ghosh ◽

Rik Ghosh ◽

Bibhas Chakraborty

Keyword(s):

Infectious Disease ◽

West Bengal ◽

Preventive Measures ◽

Growth Models ◽

Limited Resources ◽

Cumulative Number ◽

Infected People ◽

The World ◽

Entire Country ◽

The Impact

AbstractCoronavirus disease 2019 (COVID-19), a highly infectious disease, was first detected in Wuhan, China, in December 2019. The disease has spread to 212 countries and territories around the world and infected (confirmed) more than three million people. In India, the disease was first detected on 30 January 2020 in Kerala in a student who returned from Wuhan. The total (cumulative) number of confirmed infected people is more than 37000 till now across India (3 May 2020). Most of the research and newspaper articles focus on the number of infected people in the entire country. However, given the size and diversity of India, it may be a good idea to look at the spread of the disease in each state separately, along with the entire country. For example, currently, Maharashtra has more than 10000 confirmed cumulative infected cases, whereas West Bengal has less than 800 confirmed infected cases (1 May 2020). The approaches to address the pandemic in the two states must be different due to limited resources. In this article, we will focus the infected people in each state (restricting to only those states with enough data for prediction) and build three growth models to predict infected people for that state in the next 30 days. The impact of preventive measures on daily infected-rate is discussed for each state.

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Reductions of Migrant Population Reduces the Number of COVID-19 Epidemic: A Case Study in China

10.21203/rs.3.rs-112944/v1 ◽

2020 ◽

Author(s):

Lizhen Han ◽

Jinzhu Jia

Keyword(s):

Population Density ◽

Generalized Additive Model ◽

Hot Spot ◽

Additive Model ◽

Hubei Province ◽

Control Measures ◽

Cumulative Number ◽

Migrant Population ◽

High Prevalence ◽

The Impact

Abstract Background: The novel coronavirus disease (COVID-19) broke out worldwide in 2020. The purpose of this paper was to find out the impact of migrant population on the epidemic, aiming to provide data support and suggestions for control measures in various epidemic areas. Methods: Generalized additive model was utilized to model the relationship between migrant population and the cumulative number of confirmed cases of COVID-19. The difference of spatial distribution was analyzed through spatial autocorrelation and hot spot analysis. Results: Generalized additive model demonstrated that the cumulative number of confirmed cases was positively correlated with migration index and population density. The predictive results showed that if no travel restrictions are imposed on the migrant population as usual, the total cumulative number of confirmed cases of COVID-19 would have reached 27 483 (95% CI: 16 074, 48 097; the actual number was 23 177). The increase in one city (Jian) would be 577.23% (95% CI: 322.73%, 972.73%) compared to the real confirmed cases of COVID-19. The average increase in 73 cities was 85.53% (95% CI: 19.53%, 189.81%). Among the migration destinations, the number of cases in cities of Hubei province, Chongqing and Beijing was relatively high, and there were large-scale high-prevalence clusters in eastern Hubei province. Meanwhile, without restrictions on migration, the high prevalence areas in Hubei province and its surrounding areas will be further expanded. Conclusions: The reduced population mobility and population density can greatly slow down the spread of the epidemic. All epidemic areas should suspend the transportation between cities, comprehensively and strictly control the population travel and decrease the population density, so as to reduce the spread of COVID-19.

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The Impact of Isolation on the Transmission of COVID-19 and Estimation of Potential Second Epidemic in China

10.20944/preprints202003.0456.v1 ◽

2020 ◽

Author(s):

Haitao Song ◽

Fang Liu ◽

Feng Li ◽

Xiaochun Cao ◽

Hao Wang ◽

...

Keyword(s):

Virus Disease ◽

Hubei Province ◽

Healthy People ◽

Transmission Risk ◽

Initial Time ◽

Peak Time ◽

Cumulative Number ◽

First Case ◽

The Impact ◽

Close Contacts

The first case of Corona Virus Disease 2019 (COVID-19) was reported in Wuhan, China in December 2019. Since then, COVID-19 has quickly spread out to all provinces in China and over 150 countries or territories in the world. With the first level response to public health emergencies (FLRPHE) launched over the country, the outbreak of COVID-19 in China is achieving under control in China. We develop a mathematical model based on epidemiology of COVID-19, incorporating the isolation of healthy people, confirmed cases and close contacts. We calculate the basic reproduction numbers 2.5 in China (excluding Hubei province) and 2.9 in Hubei province with the initial time on January 30 which show the severe infectivity of COVID-19, and verify that the current isolation method effectively contains the transmission of COVID-19. Under the isolation of healthy people, confirmed cases and close contacts, we find a noteworthy phenomenon that is the potential second epidemic of COVID-19, and estimate the peak time and value and the cumulative number of cases. Simulations show that the isolation of close contacts tracked measure can efficiently contain the transmission of the potential second epidemic of COVID-19. With isolation of all susceptible people or all infected people or both, there is no potential second epidemic of COVID-19. Furthermore, resumption of work and study can increase the transmission risk of the potential second epidemic of COVID-19.

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Study on the Transmission of COVID-19 in Hubei Province Based on SEIR Model with Intervention Measures

10.21203/rs.3.rs-154675/v1 ◽

2021 ◽

Author(s):

Zhengsiyu HE ◽

Ling XIE ◽

Suhong LIU

Keyword(s):

Traffic Control ◽

Development Trend ◽

Hubei Province ◽

Actual Number ◽

Government Control ◽

Seir Model ◽

Human Control ◽

Intervention Measures ◽

Route Of Transmission ◽

The Government

Abstract Background At the end of 2019, an unidentified coronavirus, named as “COVID-19” by WHO, has broken out in Wuhan, Hubei Province. We aimed to simulate the development trend of COVID-19 in Wuhan and Hubei as well as estimate the number of COVID-19 cases with the government control policies and traffic control.Methods We collected the COVID-19 data in Wuhan and Hubei (January 1, 2020 to April 8, 2020) and simulated three situations about COVID-19 epidemic trend: non-interference, government controlling behavior and traffic control by the SEIR model to analyzed the development and influence of the epidemic.Results We adopted the SEIR model to estimate the number of COVID-19 cases in Hubei peaked on the 107th day without human control, and the number in Wuhan peaked on the 51st day after the lockdown of Wuhan. The number of new cases in Hubei and Wuhan presented a skewed normal distribution in the time series. Government intervention and traffic control had a certain inhibitory effect on the daily increase of COVID-19 cases. During the period from January 23 to April 8, 2020, there was a difference of 1,253,433 cases between the daily number of confirmed cases and the actual number of confirmed cases in Hubei under the simulated state of without human control. Also, there was a difference of 953,202 cases between thedaily number of confirmed cases and the actual number of confirmed cases in Wuhan under the simulated state of without human control.Conclusion The actual COVID-19 outbreak quantity conforms to the simulation results, secondly the government control behavior and the traffic control can effectively inhibit COVID-19 to spread and the efficient can reach 52%. In other countries or regions, an effective intervention measures can control the spread of the epidemic. The earlier the control started and the stronger the control intensity was, the more effective the intervention for the COVID-19 epidemic was, so appropriate to cut off the route of transmission as soon as possible.

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Hospitalization as reliable indicator of second wave COVID-19 pandemic in eight European countries

10.1101/2020.12.31.20249084 ◽

2021 ◽

Author(s):

Mattia Allieta ◽

Davide Rossi Sebastiano

Keyword(s):

European Countries ◽

Cumulative Number ◽

Actual Number ◽

Infected People ◽

Containment Measures ◽

Model Equations ◽

The Impact ◽

Second Wave ◽

Early Phases ◽

Country Specific

AbstractTime dependent reproduction number (Rt) is one of the most popular parameters to track the impact of COVID-19 pandemic. However, especially at the initial stages, Rt can be highly underestimated because of remarkable differences between the actual number of infected people and the daily incidence of people who are tested positive. Here, we present the analysis of daily cumulative number of hospitalized (HP) and intensive care unit (ICU) patients both in space and in time in the early phases of second wave COVID-19 pandemic across eight different European countries, namely Austria, Belgium, Czech Republic, France, Italy, Portugal, Spain, and United Kingdom. We derive simple model equations to fit the time dependence of these two variables where exponential behavior is observed. Growth rate constants of HP and ICU are listed, providing country-specific parameters able to estimate the burden of SARS-COV-2 infection before extensive containment measures take place. Our quantitative parameters, fully related to hospitalizations, are disentangled from the capacity range of the screening campaign, for example the number of swabs, and they cannot be directly biased by the actual number of infected people. This approach can give an array of reliable indicators which can be used by governments and healthcare systems to monitor the dynamics of COVID-19 epidemic.

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