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1.
Mathematical Biosciences and Engineering ; 17(4):3512-3519, 2020.
Article | WHO COVID | ID: covidwho-689159

ABSTRACT

The coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in the end of 2019, and soon became a serious public health threat globally Due to the unobservability, the time interval between transmission generations (TG), though important for understanding the disease transmission patterns, of COVID-19 cannot be directly summarized from surveillance data In this study, we develop a likelihood framework to estimate the TG and the pre-symptomatic transmission period from the serial interval observations from the individual transmission events As the results, we estimate the mean of TG at 4 0 days (95%CI: 3 3-4 6), and the mean of pre-symptomatic transmission period at 2 2 days (95%CI: 1 3-4 7) We approximate the mean latent period of 3 3 days, and 32 2% (95%CI: 10 3-73 7) of the secondary infections may be due to pre-symptomatic transmission The timely and effectively isolation of symptomatic COVID-19 cases is crucial for mitigating the epidemics

2.
Infect Dis Poverty ; 9(1): 96, 2020 Jul 16.
Article in English | MEDLINE | ID: covidwho-652046

ABSTRACT

BACKGROUND: Since the first case of coronavirus disease 2019 (COVID-19) in Africa was detected on February 14, 2020, the cumulative confirmations reached 15 207 including 831 deaths by April 13, 2020. Africa has been described as one of the most vulnerable region with the COVID-19 infection during the initial phase of the outbreak, due to the fact that Africa is a great commercial partner of China and some other EU and American countries. Which result in large volume of travels by traders to the region more frequently and causing African countries face even bigger health threat during the COVID-19 pandemic. Furthermore, the fact that the control and management of COVID-19 pandemic rely heavily on a country's health care system, and on average Africa has poor health care system which make it more vulnerable indicating a need for timely intervention to curtail the spread. In this paper, we estimate the exponential growth rate and basic reproduction number (R0) of COVID-19 in Africa to show the potential of the virus to spread, and reveal the importance of sustaining stringent health measures to control the disease in Africa. METHODS: We analyzed the initial phase of the epidemic of COVID-19 in Africa between 1 March and 13 April 2020, by using the simple exponential growth model. We examined the publicly available materials published by the WHO situation report to show the potential of COVID-19 to spread without sustaining strict health measures. The Poisson likelihood framework is adopted for data fitting and parameter estimation. We modelled the distribution of COVID-19 generation interval (GI) as Gamma distributions with a mean of 4.7 days and standard deviation of 2.9 days estimated from previous work, and compute the basic reproduction number. RESULTS: We estimated the exponential growth rate as 0.22 per day (95% CI: 0.20-0.24), and the basic reproduction number, R0, as 2.37 (95% CI: 2.22-2.51) based on the assumption that the exponential growth starting from 1 March 2020. With an R0 at 2.37, we quantified the instantaneous transmissibility of the outbreak by the time-varying effective reproductive number to show the potential of COVID-19 to spread across African region. CONCLUSIONS: The initial growth of COVID-19 cases in Africa was rapid and showed large variations across countries. Our estimates should be useful in preparedness planning against further spread of the COVID-19 epidemic in Africa.


Subject(s)
Basic Reproduction Number , Betacoronavirus , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Africa/epidemiology , Coronavirus Infections/transmission , Humans , Likelihood Functions , Models, Statistical , Pandemics , Pneumonia, Viral/transmission , Travel
5.
Annals of translational medicine ; 8(11):689-689, 2020.
Article | WHO COVID | ID: covidwho-631894

ABSTRACT

Background: Since the first appearance in Wuhan, China in December 2019, the novel coronavirus disease (COVID-19) has posed serious threats to the public health in many Chinese places and overseas It is essential to quantify the transmissibility on real-time basis for designing public health responses Methods: We estimated the time-varying reproduction numbers in China, Hubei province and Wuhan city by using the renewable equation determined by the serial interval (SI) of COVID-19 We compare the average reproduction numbers in different periods of time to explore the effectiveness of the public health control measures against the COVID-19 epidemic Results: We estimated the reproduction numbers at 2 61 (95% CI: 2 47-2 75), 2 76 (95% CI: 2 54-2 95) and 2 71 (95% CI: 2 43-3 01) for China, Hubei province and Wuhan respectively We found that the reproduction number largely dropped after the city lockdown As of February 16, the three reproduction numbers further reduced to 0 98, 1 14 and 1 41 respectively Conclusions: The control of COVID-19 epidemic was effective in substantially reducing the disease transmissibility in terms of the reproduction number in China reduced to 0 98 as of February 16 At the same time, the reproduction number in Wuhan was probably still larger than 1, and thus the enhancement in the public health control was recommended to maintain

6.
Int J Infect Dis ; 98: 67-70, 2020 Jun 26.
Article in English | MEDLINE | ID: covidwho-619520

ABSTRACT

We compared the COVID-19 and 1918-19 influenza pandemics in the United Kingdom. We found that the ongoing COVID-19 wave of infection matched the major wave of the 1918-19 influenza pandemic surprisingly well, with both reaching similar magnitudes (in terms of estimated weekly new infections) and spending the same duration with over five cases per 1000 inhabitants over the previous two months. We also discussed the similarities in epidemiological characteristics between these two pandemics.

7.
Int J Infect Dis ; 96: 284-287, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-379136

ABSTRACT

BACKGROUNDS: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused a large outbreak of coronavirus disease, COVID-19, in Wuhan, China, since December 2019. COVID-19 soon spread to other regions of China and overseas. In Hong Kong, local mitigation measures were implemented since the first imported case was confirmed on January 23, 2020. Here we evaluated the temporal variation of detection delay from symptoms onset to laboratory confirmation of SARS-CoV-2 in Hong Kong. METHODS: A regression model is adopted to quantify the association between the SARS-CoV-2 detection delay and calendar time. The association is tested and further validated by a Cox proportional hazard model. FINDINGS: The estimated median detection delay was 9.5 days (95%CI: 6.5-11.5) in the second half of January, reduced to 6.0 days (95%CI: 5.5-9.5) in the first half of February 2020. We estimate that SARS-CoV-2 detection efficiency improved at a daily rate of 5.40% (95%CI: 2.54-8.33) in Hong Kong. CONCLUSIONS: The detection efficiency of SARS-CoV-2 was likely being improved substantially in Hong Kong since the first imported case was detected. Sustaining enforcement in timely detection and other effective control measures are recommended to prevent the SARS-CoV-2 infection.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Coronavirus Infections/epidemiology , Delayed Diagnosis , Disease Outbreaks , Hong Kong/epidemiology , Humans , Pandemics , Pneumonia, Viral/epidemiology , Proportional Hazards Models
8.
Transbound Emerg Dis ; 2020 May 26.
Article in English | MEDLINE | ID: covidwho-378330

ABSTRACT

The novel coronavirus disease (COVID-19) poses a serious threat to global public health and economics. Serial interval (SI), time between the onset of symptoms of a primary case and a secondary case, is a key epidemiological parameter. We estimated SI of COVID-19 in Shenzhen, China based on 27 records of transmission chains. We adopted three parametric models: Weibull, lognormal and gamma distributions, and an interval-censored likelihood framework. The three models were compared using the corrected Akaike information criterion (AICc). We also fitted the epidemic curve of COVID-19 to the logistic growth model to estimate the reproduction number. Using a Weibull distribution, we estimated the mean SI to be 5.9 days (95% CI: 3.9-9.6) with a standard deviation (SD) of 4.8 days (95% CI: 3.1-10.1). Using a logistic growth model, we estimated the basic reproduction number in Shenzhen to be 2.6 (95% CI: 2.4-2.8). The SI of COVID-19 is relatively shorter than that of SARS and MERS, the other two betacoronavirus diseases, which suggests the iteration of the transmission may be rapid. Thus, it is crucial to isolate close contacts promptly to effectively control the spread of COVID-19.

9.
Int J Infect Dis ; 96: 128-130, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-281360

ABSTRACT

Owing to the frequent travel connections between Wuhan and Zhejiang, Zhejiang was the third worst-affected province in China with 1,205 cases confirmed before 26 February 2020. The transmissibility of the 2019 novel coronavirus disease was monitored in Zhejiang, accounting for the transmissions from imported cases. Even though Zhejiang was one of the worst-affected provinces, an interruption of disease transmission (i.e. instantaneous reproduction numbers <1) was observed in early/mid-February after a comprehensive set of interventions combating the outbreak.


Subject(s)
Betacoronavirus , Coronavirus Infections/transmission , Pneumonia, Viral/transmission , Basic Reproduction Number , China/epidemiology , Coronavirus Infections/epidemiology , Humans , Pandemics , Pneumonia, Viral/epidemiology
10.
Int J Infect Dis ; 96: 284-287, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-264971

ABSTRACT

BACKGROUNDS: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused a large outbreak of coronavirus disease, COVID-19, in Wuhan, China, since December 2019. COVID-19 soon spread to other regions of China and overseas. In Hong Kong, local mitigation measures were implemented since the first imported case was confirmed on January 23, 2020. Here we evaluated the temporal variation of detection delay from symptoms onset to laboratory confirmation of SARS-CoV-2 in Hong Kong. METHODS: A regression model is adopted to quantify the association between the SARS-CoV-2 detection delay and calendar time. The association is tested and further validated by a Cox proportional hazard model. FINDINGS: The estimated median detection delay was 9.5 days (95%CI: 6.5-11.5) in the second half of January, reduced to 6.0 days (95%CI: 5.5-9.5) in the first half of February 2020. We estimate that SARS-CoV-2 detection efficiency improved at a daily rate of 5.40% (95%CI: 2.54-8.33) in Hong Kong. CONCLUSIONS: The detection efficiency of SARS-CoV-2 was likely being improved substantially in Hong Kong since the first imported case was detected. Sustaining enforcement in timely detection and other effective control measures are recommended to prevent the SARS-CoV-2 infection.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Coronavirus Infections/epidemiology , Delayed Diagnosis , Disease Outbreaks , Hong Kong/epidemiology , Humans , Pandemics , Pneumonia, Viral/epidemiology , Proportional Hazards Models
11.
Ann. Transl. Med. ; 7(8)20200401.
Article in English | ELSEVIER | ID: covidwho-251829

ABSTRACT

Background: The coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China on December 2019 in patients presenting with atypical pneumonia. Although 'city-lockdown' policy reduced the spatial spreading of the COVID-19, the city-level outbreaks within each city remain a major concern to be addressed. The local or regional level disease control mainly depends on individuals self-administered infection prevention actions. The contradiction between choice of taking infection prevention actions or not makes the elimination difficult under a voluntary acting scheme, and represents a clash between the optimal choice of action for the individual interest and group interests. Methods: We develop a compartmental epidemic model based on the classic susceptible-exposed-infectious-recovered model and use this to fit the data. Behavioral imitation through a game theoretical decision-making process is incorporated to study and project the dynamics of the COVID-19 outbreak in Wuhan, China. By varying the key model parameters, we explore the probable course of the outbreak in terms of size and timing under several public interventions in improving public awareness and sensitivity to the infection risk as well as their potential impact. Results: We estimate the basic reproduction number, R0, to be 2.5 (95% CI: 2.4-2.7). Under the current most realistic setting, we estimate the peak size at 0.28 (95% CI: 0.24-0.32) infections per 1,000 population. In Wuhan, the final size of the outbreak is likely to infect 1.35% (95% CI: 1.00-2.12%) of the population. The outbreak will be most likely to peak in the first half of February and drop to daily incidences lower than 10 in June 2020. Increasing sensitivity to take infection prevention actions and the effectiveness of infection prevention measures are likely to mitigate the COVID-19 outbreak in Wuhan. Conclusions: Through an imitating social learning process, individual-level behavioral change on taking infection prevention actions have the potentials to significantly reduce the COVID-19 outbreak in terms of size and timing at city-level. Timely and substantially resources and supports for improving the willingness-to-act and conducts of self-administered infection prevention actions are recommended to reduce to the COVID-19 associated risks.

12.
Int J Infect Dis ; 95: 308-310, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-209062

ABSTRACT

The novel coronavirus disease 2019 (COVID-19) outbreak has caused 6088 cases and 41 deaths in Republic of Korea, and 3144 cases and 107 death in Italy by 5 March 2020, respectively. We modelled the transmission process in the Republic of Korea and Italy with a stochastic model, and estimated the basic reproduction number R0 as 2.6 (95% CI: 2.3-2.9) or 3.2 (95% CI: 2.9-3.5) in the Republic of Korea, under the assumption that the exponential growth starting on 31 January or 5 February 2020, and 2.6 (95% CI: 2.3-2.9) or 3.3 (95% CI: 3.0-3.6) in Italy, under the assumption that the exponential growth starting on 5 February or 10 February 2020, respectively.


Subject(s)
Basic Reproduction Number , Betacoronavirus , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Disease Outbreaks , Humans , Italy/epidemiology , Pandemics , Republic of Korea/epidemiology , Time Factors
14.
Int J Infect Dis ; 94: 145-147, 2020 May.
Article in English | MEDLINE | ID: covidwho-142367

ABSTRACT

Asymptomatic transmission of the coronavirus disease 2019 is an important topic. A recent study in China showed that transmissibility of the asymptomatic cases is comparable to that of symptomatic cases. Here, we discuss that the conclusion may depend on how we interpret the data. To the best of our knowledge, this is the first time the relative transmissibility of asymptomatic COVID-19 infections is quantified.


Subject(s)
Asymptomatic Infections , Betacoronavirus , Coronavirus Infections/transmission , Pneumonia, Viral/transmission , China , Humans , Pandemics
15.
Int J Infect Dis ; 95: 308-310, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-101979

ABSTRACT

The novel coronavirus disease 2019 (COVID-19) outbreak has caused 6088 cases and 41 deaths in Republic of Korea, and 3144 cases and 107 death in Italy by 5 March 2020, respectively. We modelled the transmission process in the Republic of Korea and Italy with a stochastic model, and estimated the basic reproduction number R0 as 2.6 (95% CI: 2.3-2.9) or 3.2 (95% CI: 2.9-3.5) in the Republic of Korea, under the assumption that the exponential growth starting on 31 January or 5 February 2020, and 2.6 (95% CI: 2.3-2.9) or 3.3 (95% CI: 3.0-3.6) in Italy, under the assumption that the exponential growth starting on 5 February or 10 February 2020, respectively.


Subject(s)
Basic Reproduction Number , Betacoronavirus , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Disease Outbreaks , Humans , Italy/epidemiology , Pandemics , Republic of Korea/epidemiology , Time Factors
16.
Int J Infect Dis ; 94: 145-147, 2020 May.
Article in English | MEDLINE | ID: covidwho-72514

ABSTRACT

Asymptomatic transmission of the coronavirus disease 2019 is an important topic. A recent study in China showed that transmissibility of the asymptomatic cases is comparable to that of symptomatic cases. Here, we discuss that the conclusion may depend on how we interpret the data. To the best of our knowledge, this is the first time the relative transmissibility of asymptomatic COVID-19 infections is quantified.


Subject(s)
Asymptomatic Infections , Betacoronavirus , Coronavirus Infections/transmission , Pneumonia, Viral/transmission , China , Humans , Pandemics
17.
Infect Control Hosp Epidemiol ; : 1-2, 2020 Apr 16.
Article in English | MEDLINE | ID: covidwho-65862
19.
Int J Infect Dis ; 94: 29-31, 2020 May.
Article in English | MEDLINE | ID: covidwho-8625

ABSTRACT

As of March 1, 2020, Iran had reported 987 novel coronavirus disease (COVID-19) cases, including 54 associated deaths. At least six neighboring countries (Bahrain, Iraq, Kuwait, Oman, Afghanistan, and Pakistan) had reported imported COVID-19 cases from Iran. In this study, air travel data and the numbers of cases from Iran imported into other Middle Eastern countries were used to estimate the number of COVID-19 cases in Iran. It was estimated that the total number of cases in Iran was 16 533 (95% confidence interval: 5925-35 538) by February 25, 2020, before the UAE and other Gulf Cooperation Council countries suspended inbound and outbound flights from Iran.


Subject(s)
Betacoronavirus/physiology , Coronavirus Infections/transmission , Pneumonia, Viral/transmission , Travel-Related Illness , Air Travel , Humans , Iran , Pandemics
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