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1.
Results Phys ; 38: 105653, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1867748

ABSTRACT

Reinfection and reactivation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have recently raised public health pressing concerns in the fight against the current pandemic globally. In this study, we propose a new dynamic model to study the transmission of the coronavirus disease 2019 (COVID-19) pandemic. The model incorporates possible relapse, reinfection and environmental contribution to assess the combined effects on the overall transmission dynamics of SARS-CoV-2. The model's local asymptotic stability is analyzed qualitatively. We derive the formula for the basic reproduction number ( R 0 ) and final size epidemic relation, which are vital epidemiological quantities that are used to reveal disease transmission status and guide control strategies. Furthermore, the model is validated using the COVID-19 reported situations in Saudi Arabia. Moreover, sensitivity analysis is examined by implementing a partial rank correlation coefficient technique to obtain the ultimate rank model parameters to control or mitigate the pandemic effectively. Finally, we employ a standard Euler technique for numerical simulations of the model to elucidate the influence of some crucial parameters on the overall transmission dynamics. Our results highlight that contact rate, hospitalization rate, and reactivation rate are the fundamental parameters that need particular emphasis for the prevention, mitigation and control.

3.
Infect Dis Model ; 7(2): 189-195, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1867204

ABSTRACT

The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess (DP) ship has caused over 634 cases as of February 20, 2020. We model the transmission process on DP ship as a stochastic branching process, and estimate the reproduction number at the innitial phase of 2.9 (95%CrI: 1.7-7.7). The epidemic doubling time is 3.4 days, and thus timely actions on COVID-19 control were crucial. We estimate the COVID-19 transmissibility reduced 34% after the quarantine program on the DP ship which was implemented on February 5. According to the model simulation, relocating the population at risk may sustainably decrease the epidemic size, postpone the timing of epidemic peak, and thus relieve the tensive demands in the healthcare. The lesson learnt on the ship should be considered in other similar settings.

4.
Chinese Journal of School Health ; 43(3):411-412, 2022.
Article in Chinese | GIM | ID: covidwho-1865666

ABSTRACT

Objective: To understand anxiety status among students with hearing loss under the epidemic of novel coronarirus pneumonia, and to provide evidence for promoting mental health of hearing impaired students.

5.
J Theor Biol ; 542: 111105, 2022 Jun 07.
Article in English | MEDLINE | ID: covidwho-1814837

ABSTRACT

As the COVID-19 pandemic continues, genetic mutations in SARS-CoV-2 emerge, and some of them are found more contagious than the previously identified strains, acting as the major mechanism for many large-scale epidemics. The transmission advantage of mutated variants is widely believed as an innate biological feature that is difficult to be altered by artificial factors. In this study, we explore how non-pharmaceutical interventions (NPI) may affect transmission advantage. A two-strain compartmental epidemic model is proposed and simulated to investigate the biological mechanism of the relationships among different NPIs, the changes in transmissibility of each strain and transmission advantage. Although the NPIs are effective in flattening the epidemic curve, we demonstrate that NPIs probably lead to a decline in transmission advantage, which is likely to occur if the NPIs become intensive. Our findings uncover the mechanistic relationship between NPIs and transmission advantage dynamically, and highlight the important role of NPIs not only in controlling the intensity of epidemics but also in slowing or even containing the growth of the proportion of variants.


Subject(s)
COVID-19 , Epidemics , COVID-19/epidemiology , Humans , Models, Theoretical , Pandemics , SARS-CoV-2/genetics
6.
Infect Dis Model ; 7(2): 25-32, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1796731

ABSTRACT

Objectives: Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which makes the serological data difficult to interpret. In this work, we aim to solve this issue. Methods: We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-19 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. Results: Our model simulated seroprevalence matchs the reported seroprevalence in most of the 12 Indian cities. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. Conclusions: Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence.

7.
J Travel Med ; 2022 Apr 18.
Article in English | MEDLINE | ID: covidwho-1795212

ABSTRACT

Using two early transmission chains in Hong Kong, the estimated R and k were 1.34 (95%CrI: 0.94-2.19) and 0.33 (95%CrI: 0.17-0.62) respectively, inferring 20.3% (95%CrI: 12.7%-29.6%) cases were responsible for 80% of the transmissions of the Omicron epidemic. Compared with Omicron BA.1, Omicron BA.2 had a greater superspreading potential.

8.
Int J Public Health ; 67: 1604363, 2022.
Article in English | MEDLINE | ID: covidwho-1792859

ABSTRACT

Objectives: To determine the association of sleep with mental health among Hong Kong community-dwelling older men in the context of the COVID-19 pandemic. Methods: This additional analysis was derived from the community-dwelling men aged >60 recruited during three COVID-19 outbreaks (i.e., pre-outbreak, between the second and third wave, and during the third wave) in Hong Kong from July 2019 to September 2020. Sleep and mental health were measured by Pittsburgh Sleep Quality Index questionnaire and Hospital Anxiety and Depression Scale, respectively. Multivariate logistic regression models were performed for the associations between sleep and mental health after considering the outbreaks' impact. Results: Subjects enrolled between the second and third wave tended to have better sleep but worse mental health. Positive associations between poor sleep and depression (AOR = 3.27, 95% CI: 1.60-7.03) and anxiety (AOR = 2.40, 95% CI: 1.07-5.76) were observed. The period "between second and third wave" was positively associated with depression (AOR = 2.65, 95% CI: 1.22-5.83), showing an additive interaction with poor sleep. Conclusion: The positive association between poor sleep and depression was aggravated by the period "between the second and third wave" among community-dwelling older males in Hong Kong.


Subject(s)
COVID-19 , Sleep Initiation and Maintenance Disorders , Aged , Anxiety/epidemiology , COVID-19/epidemiology , Cross-Sectional Studies , Depression/epidemiology , Disease Outbreaks , Hong Kong/epidemiology , Humans , Independent Living , Male , Mental Health , Pandemics , SARS-CoV-2 , Sleep , Sleep Initiation and Maintenance Disorders/epidemiology
9.
Journal of theoretical biology ; 2022.
Article in English | EuropePMC | ID: covidwho-1749848

ABSTRACT

As the COVID-19 pandemic continues, genetic mutations in SARS-CoV-2 emerge, and some of them are found more contagious than the previously identified strains, acting as the major mechanism for many large-scale epidemics. The transmission advantage of mutated variants is widely believed as an innate biological feature that cannot be altered by artificial factors. In this study, we explore how non-pharmaceutical interventions (NPI) may affect transmission advantage. A two-strain compartmental epidemic model is proposed and simulated to investigate the biological mechanism of the relationships among different NPIs, the changes in transmissibility of each strain and transmission advantage. Although the NPIs are effective in flattening the epidemic curve, we demonstrate that NPIs probably lead to a decline in transmission advantage, which is likely to occur if the NPIs become intensive. Our findings uncover the mechanistic relationship between NPIs and transmission advantage dynamically, and highlight the important role of NPIs not only in controlling the intensity of epidemics but also in showing or even containing the growth of the proportion of mutated variants.

10.
Iranian Journal of Kidney Diseases ; 15(5):323-326, 2021.
Article in English | ProQuest Central | ID: covidwho-1738035

ABSTRACT

A growing body of evidence points out at chronic kidney disease (CKD) as a major risk factor for severe COVID-19, increasing also the respective mortality risk. Preventive measures, rapid monitoring organ function and interventions capable of preventing multiorgan failures are of great importance to reduce adverse outcomes in COVID-19 patients with CKD. While efforts are underway to carry out indirect protection interventions and large-scale vaccination to achieve herd immunity in the general population, direct protection of patients with CKD through rapid vaccination trials are necessary since uraemia and immunosuppressive agents could have a negative impact on vaccination responses of CDK patients. More epidemiological data are needed for in-depth understanding of the course and outcome of COVID-19 in CKD patients, supporting clinical decision-making. DOI: 10.52547/ijkd.6797

11.
Infect Dis Model ; 7(2): 1-24, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1729801

ABSTRACT

Since March 11th, 2020, COVID-19 has been a global pandemic for more than one years due to a long and infectious incubation period. This paper establishes a heterogeneous epidemic model that divides the incubation period into infectious and non-infectious and employs the Bayesian framework to model the 'Diamond Princess' enclosed space incident. The heterogeneity includes two different identities, two transmission methods, two different-size rooms, and six transmission stages. This model is also applicable to similar mixed structures, including closed schools, hospitals, and communities. As the COVID-19 pandemic continues, our mathematical modeling can provide management insights to the governments and policymakers on how the COVID-19 disease has spread and what prevention strategies still need to be taken.

13.
Math Biosci Eng ; 19(4): 3591-3596, 2022 02 07.
Article in English | MEDLINE | ID: covidwho-1704200

ABSTRACT

In this work, we report a large-scale synchronized replacement pattern of the Alpha (B.1.1.7) variant by the Delta (B.1.617.2) variant of SARS-COV-2. We argue that this phenomenon is associated with the invasion timing and the transmissibility advantage of the Delta (B.1.617.2) variant. Alpha (B.1.1.7) variant skipped some countries/regions, e.g. India and neighboring countries/regions, which could have led to a mild first wave before the invasion of the Delta (B.1.617.2) variant, in term of reported COVID-deaths per capita.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Humans , India/epidemiology , Pandemics , SARS-CoV-2/genetics
14.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-317615

ABSTRACT

Background: The COVID-19 pandemic has caused tremendous impact on global health and economics. The impact in African countries has not been investigated through fitting epidemic model to the reported COVID-19 deaths. Method: We downloaded data for the twelve most-affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying effective reproduction number (B) and infection attack rate (IAR). We developed a simple epidemic model and fitted the model to reported COVID-19 deaths in 12 African countries, using iterated filtering and allowing flexible transmission rate. Results: : We found high heterogeneity in the case-fatality rate across countries, which may be due to different reporting or testing efforts. We found that South Africa, Tunisia, and Libya were hit hardest with a relatively higher a and infection attack rate Conclusion: To effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvement in socio-economic wellbeing, health care system, water supply, awareness campaigns).

15.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-317614

ABSTRACT

Background: Since the first case of coronavirus disease 2019 (COVID-19) was detected on February 14, 2020, the cumulative confirmations reached 15207 including 831 deaths by April 13, 2020. 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. Results: We estimated the exponential growth rate as 0.22 per day (95%CI: 0.20 – 0.24), and the basic reproduction number, R0, to be 2.37 (95%CI: 2.22-2.51) based on the assumption that the exponential growth starting from 1 March 2020. Conclusion: 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. Trial registration: NA

16.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-317613

ABSTRACT

Background: The coronavirus disease 2019 (known as COVID-19) pandemic caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) appeared in Wuhan, China has rapidly spread to over 200 countries and territories. In Nigeria, the Kano State Ministry of Health has confirmed its first case of COVID-19 on April 11, 2020, and since then there might have been issues of under-ascertainment that occurred roughly from 22 to 27 April 2020. As of 4 October 2020, there were 1738 reported COVID-19 cases in Kano with 54 associated deaths. In this work, we estimate the number of under-ascertainment cases and the basic reproduction number, B, of COVID-19 in Kano, Nigeria. We also predict the number of COVID-19 cases in the short term. Methods: We employ the exponential growth and modelled the outbreak curve of COVID-19 cases, in Kano, Nigeria from 11 to 30 April 2020. We estimated the number of under-ascertainment cases using the maximum likelihood estimation. We adopted the SI estimated for Hong Kong as approximations of the unknown SI for COVID-19 in Kano to estimate the a. We use ARIMA model to provide a short term (15 days) prediction of the COVID-19 cases in Kano, Nigeria. Results: We revealed that the initial growth phase mimic an exponential growth pattern. We found that the under-ascertainment was likely to have resulted in 213 (95% CI: 106−346) unreported cases from 22 to 27 April 2020. The reporting rate after 27 April 2020 increase up to 10-fold compared to the scenario from 22 to 27 April 2020 on average. We estimated the c of COVID-19 in Kano as 2.74 (95% CI: 2.53−2.96). We forecasted that the total number of COVID-19 cases in Kano to be 1067 (95% CI: 883, 2137) by June 6, 2020. Conclusion: The under-ascertainment likely exists during the fourth week of April, 2020 and should be regarded in the future analysis/investigation.

17.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-314769

ABSTRACT

Background: For Severe Acute Respiratory Syndrome Coronavirus-2, the investigation of the heterogeneity of individual infectiousness is important due to the recorded widespread cross reactive immunity of general population that can alter transmission dynamics. We therefore aimed to understand how SARS-COV-2 transmits in the general population in relation to age. Design: Using a sample of infected population with SARS-COV-2 in close geographical proximity to the initial Severe Advanced Respiratory Syndrome-1 (SARS-1) outbreak, we explored the association between infector’s age and dispersion (or heterogeneity) of individual infectiousness (k) in order to investigate the relatedness with the age of an individual’s capability to disperse SARS-COV-2. Results: We have found a negative association between k and increase of infector’s age. Significantly this becomes more evident for the age group of 20-60 years comparing with the infectors of younger age. Conclusions: Non pharmaceutical interventions can be effective to age group between 20-60 years whereas in youngsters and older patients containment of spreading must be made by other means to be effective. Immunity differences between age groups may reflect their differences in heterogeneity predicted by variance in dispersion parameter (k) .

18.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-312175

ABSTRACT

Objectives: Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which make the serological data difficult to interpret. In this work, we aim to solve this issue. Methods. We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-10 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. Results. Our model simulated seroprevalence match the reported seroprevalence in most (but not all) of the 12 Indian cities we considered. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. Conclusions. Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence.

19.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-307548

ABSTRACT

Background: Asymptomatic infection of SARS-CoV-2 may lead to silent community transmission and compromise pandemic control measures of COVID-19. We aimed to estimate the rate of asymptomatic COVID-19 infection from published studies, and compare this rate among different patient groups. Methods: The electronic databases including Medline, Embase, PubMed, and three Chinese electronic databases (The Chinese National Knowledge Infrastructure (CNKI), WanFang Data, and VIP) were searched. Studies with sample size (or number of subjects) not less than 5 were included. The STATA command ‘ Metaprop ’ was implemented to conduct meta-analysis for the pooled rate estimates of asymptomatic infections with exact binomial and score test-based 95% confidence intervals (CIs). Results: A total of 12,713 COVID-19 patients in 136 studies were included in the meta-analysis, including 2,785 asymptomatic infections. The overall rate of asymptomatic infection was 15.1% (95% CI: 12.0%-18.4%). Subgroup analysis showed that the rate was significantly higher in pregnant women (36.3%, 95% CI: 15.7%-59.6%), children (29.4%, 17.4%-42.9%), and studies for screening settings (25.3%, 15.4%-36.5%) conducted on or after 01 March 2020 (27.8%, 15.7%-41.7%). In terms of geographical regions, the rate was the highest in Asia (excluding China) (27.4%, 14.3%-42.6%), followed by Europe (22.7%, 6.3%-44.9%), the US (15.9%, 8.9%-24.3%), and China (13.1%, 10.2%-16.3%). Conclusions: High proportion of asymptomatic infection were observed in pregnant women, children, European residents, screening programmes, and in studies conducted in and after March 2020. Our findings help inform the true burden of COVID-19 among different groups of cases, and provide information on cost-effective strategies of identifying and tracing asymptomatic infections.

20.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325250

ABSTRACT

The COVID-19 pandemic poses a serious threat to global health, and one of the key epidemiological factors that shape the transmission of COVID-19 is its serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, slight discrepancies in SI across different transmission generations are observed from the aggregated statistics in recent studies. To explore the change in SI across transmission generations, we develop a likelihood-based statistical inference framework to examine and quantify the change in SI. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that the individual SI of COVID-19 is likely to shrink with a rate of 0.72 per generation and 95%CI: (0.54, 0.96) as the transmission generation increases. We speculate that the shrinkage in SI is an outcome of competition among multiple candidate infectors within a cluster of cases. The shrinkage in SI may speed up the transmission process, and thus the nonpharmaceutical interventive strategies are crucially important to mitigate the COVID-19 epidemic.

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