scholarly journals Investigations on the Control of COVID-19 and Suggestions for Re-Open Based on Model Study

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 22
Author(s):  
Yongxue Chen ◽  
Jiayu Shen ◽  
Shijing Li ◽  
Yongxian Wen
Keyword(s):  
Dynamic Model ◽  
Incubation Period ◽  
High Probability ◽  
Reproduction Number ◽  
Social Disruption ◽  
Model Study ◽  
Epidemic Period ◽  
Contact Tracking ◽  
At Home

Data-based analysis gives out an estimation of the incubation period. A dynamic model is established and discussed. Disease reproduction number reveals the high probability of COVID-19 pandemic, but strengthening the exposure of asymptomatic people will help to curb the transmission, and measures of contact-tracking and stay-at-home play a replaceable role. Discussions point out that social disruption can be avoided if the contact tracking rate can be more than 0.5. Investigations for re-opening show that a city of the same size as Wuhan can be reopened if new cases are continuously below 1000 for a few days and when they are less than 500, with the assurance of contact tracking associated with extensive testing. In short, tracking and testing are the prioritized strategies, while maintaining awareness can shorten the epidemic period and mobility restrictions can be avoided.

scholarly journals Epidemiological Analysis of the 2019 Dengue Epidemic in Bhutan

2021 ◽  
Vol 18 (1) ◽  
pp. 354
Author(s):  
Tsheten Tsheten ◽  
Angus Mclure ◽  
Archie C. A. Clements ◽  
Darren J. Gray ◽  
Tenzin Wangdi ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Monsoon Season ◽  
Outbreak Response ◽  
Spatial And Temporal Patterns ◽  
Epidemiological Analysis ◽  
Preparedness Plan ◽  
Epidemic Period ◽  
Timely Response ◽  
National Preparedness ◽  
Dengue Epidemic

Bhutan experienced its largest and first nation-wide dengue epidemic in 2019. The cases in 2019 were greater than the total number of cases in all the previous years. This study aimed to characterize the spatiotemporal patterns and effective reproduction number of this explosive epidemic. Weekly notified dengue cases were extracted from the National Early Warning, Alert, Response and Surveillance (NEWARS) database to describe the spatial and temporal patterns of the epidemic. The time-varying, temperature-adjusted cohort effective reproduction number was estimated over the course of the epidemic. The dengue epidemic occurred between 29 April and 8 December 2019 over 32 weeks, and included 5935 cases. During the epidemic, dengue expanded from six to 44 subdistricts. The effective reproduction number was <3 for most of the epidemic period, except for a ≈1 month period of explosive growth, coinciding with the monsoon season and school vacations, when the effective reproduction number peaked >30 and after which the effective reproduction number declined steadily. Interventions were only initiated 6 weeks after the end of the period of explosive growth. This finding highlights the need to reinforce the national preparedness plan for outbreak response, and to enable the early detection of cases and timely response.

scholarly journals Research on HIV Spreading by Macroscopic Based on Molecular Phylogeny in Anhui Province, China

2020 ◽  
Author(s):  
Zhen Wang ◽  
Zhiyuan Zhang ◽  
Xin Jin ◽  
Hui Wu ◽  
Yuhua Ruan ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Dynamic Model ◽  
Hiv Transmission ◽  
Reproduction Number ◽  
Anhui Province ◽  
Entire Population ◽  
Bisexual Men ◽  
Gene Sequences ◽  
Hiv Virus ◽  
History Of

Abstract BackgroundAcquired Immune Deficiency Syndrome (AIDS) is a global pandemic. The disease remains the most challenging public health issue of common concern in all countries because of its high fatality rate, the widespread susceptibility of the entire population to HIV, and the lack of an effective vaccine or cure. Therefore, it is of great significance to study and analyze the spread of HIV/AIDS.MethodsWe applied Bayesian phylogenetic methods to the gene sequence of the two main subtypes (CRF 01AE and CRF 07BC, 340 in total) of the HIV virus in Anhui Province to infer the nearest ancestor and its effective reproduction number (Re) to trace the history of HIV transmission in Anhui Province. Based on the characteristics of HIV transmission between heterosexuals and homosexuals, we established a dynamic model to predict the future trend of HIV drug resistance transmission. Through fitting the two effective reproduction numbers (Re) from the two different methods above, we got some important parameter values. By analyzing the sensitive factors affecting the transmission situation, we proposed relevant measures to reduce the transmission of HIV-resistant strains and effectively prevent and control the HIV epidemic.ResultsThrough the study of gene sequences, it was inferred that the nearest ancestor of the 150 CRF 01AE subtypes was in 1982, while the nearest ancestor of the 190 CRF 07BC subtypes was later (1992). Moreover, the effective reproduction number Re of HIV transmission in Anhui Province has been stable at first, then increased, and then remained stable, from 1.33 in 1992 to 2.20 in 2018. The study of macro-transmission dynamics model found that simply increasing the treatment rate had little effect on reducing the infection rate of the entire population, but would lead to the increase of drug resistance rate instead. This may be due to the inverse ratio between the prevalence of HIV and the lifespan of infected patients after treatment. According to the Sensitivity analysis, a more effective way to control transmission is reducing the number of sexual partners of the MSM population. In addition, we also have unexpected new findings regarding the traditional belief that "bisexual men play a bridge role in the transmission of HIV virus": if simply cutting o the sexual relationship between women and bisexual men, the HIV epidemic will be significantly enhanced instead.ConclusionStudy on gene sequences told us the history of HIV/AIDS spreading in Anhui province and the dynamic model told us its future. The link between them is the the effective reproduction number Re.

Chickenpox in Leukemic Patients— Factors in Prognosis

PEDIATRICS ◽  
1964 ◽  
Vol 34 (4) ◽  
pp. 562-564
Author(s):  
GERALD BODEY ◽  
EUGENE MCKELVEY ◽  
MYRON KARON
Keyword(s):  
Complete Remission ◽  
Acute Leukemia ◽  
Incubation Period ◽  
Partial Remission ◽  
Ample Evidence ◽  
The Past ◽  
Frequent Administration ◽  
Fatal Complications ◽  
Platelet Transfusions ◽  
At Home

There is now ample evidence that chickenpox can produce fatal complications in patients with acute leukemia. The prognosis, however, is not invariably bad. In the past three years, we have observed twelve cases of varicella in leukemic patients, all of whom recovered (Table I). Nine of the twelve cases occurred during two epidemics on the childrens' ward. The incubation period ranged from 14-24 days with a median of 19 days. The remaining three were infected at home. Eight children were in marrow remission, two were in partial remission (Nos. 9 & 10) and two had relapsed (Nos. 11 & 12). Three patients were severely ill (Nos. 10, 11 & 12), and none of these was in complete remission. Patient No. 11 had profound leukopenia and thrombocytopenia, but did not develop hemorrhagic lesions probably because of the frequent administration of platelet transfusions.

scholarly journals Incubation Period and Reproduction Number for Novel Coronavirus 2019 (COVID-19) Infections in India

2020 ◽  
Vol 32 (8) ◽  
pp. 458-460
Author(s):  
Seema Rajesh Patrikar ◽  
Atul Kotwal ◽  
Vijay K. Bhatti ◽  
Amitav Banerjee ◽  
Kunal Chatterjee ◽  
...  
Keyword(s):  
Incubation Period ◽  
Reproduction Number ◽  
Novel Coronavirus

scholarly journals SARS-CoV-2 transmission potential and rural-urban disease burden disparities across Alabama, Louisiana, and Mississippi, March 2020 — May 2021

2021 ◽  
Author(s):  
Sylvia Kesewaa Ofori ◽  
Chigozie Alexandra Ogwara ◽  
Seoyon Kwon ◽  
Xinyi Hua ◽  
Kamryn M. Martin ◽  
...  
Keyword(s):  
Disease Burden ◽  
Reproduction Number ◽  
Face Mask ◽  
R Package ◽  
Transmission Potential ◽  
Southern States ◽  
October 17 ◽  
Health Promotion Interventions ◽  
Vaccination Campaigns ◽  
At Home

Purpose: To quantify and compare SARS-CoV-2 transmission potential across Alabama, Louisiana, and Mississippi and selected counties with populations in the 50th, 75th, and 100th percentile. Methods: To determine the time-varying reproduction number Rt of SARS-CoV-2, we applied the R package EpiEstim to the time series of daily incidence of confirmed cases. Median Rt percentage change when policies changed was determined. Linear regression was performed between log10-transformed cumulative incidence and log10-transformed population size at four time points. Results: Stay-at-home orders, face mask mandates, and vaccinations were associated with the most significant reductions in SARS-CoV-2 transmission in the three southern states. Rt across the three states decreased significantly by 20% following stay-at-home orders. We observed varying degrees of reductions in Rt across states following other policies. Rural Alabama counties experienced higher per capita cumulative cases relative to urban ones as of June 17 and October 17, 2020. Meanwhile, Louisiana and Mississippi saw the disproportionate impact of SARS-CoV-2 in rural counties compared to urban ones throughout the study period. Conclusion: State and county policies had an impact on local pandemic trajectories. The rural-urban disparities in case burden call for evidence-based approaches in tailoring health promotion interventions and vaccination campaigns to rural residents.

scholarly journals Effects of prolonged incubation period and centralized quarantine on the COVID-19 outbreak in Shijiazhuang, China: a modeling study

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wenlong Zhu ◽  
Mengxi Zhang ◽  
Jinhua Pan ◽  
Ye Yao ◽  
Weibing Wang
Keyword(s):  
Nucleic Acid ◽  
Incubation Period ◽  
Likelihood Method ◽  
Cumulative Number ◽  
Nucleic Acid Testing ◽  
Prolonged Incubation ◽  
Serial Interval ◽  
Epidemic Period ◽  
Epidemiological Characteristics ◽  
The Impact

Abstract Background From 2 January to 14 February 2021, a local outbreak of COVID-19 occurred in Shijiazhuang, the capital city of Hebei Province, with a population of 10 million. We analyzed the characteristics of the local outbreak of COVID-19 in Shijiazhuang and evaluated the effects of serial interventions. Methods Publicly available data, which included age, sex, date of diagnosis, and other patient information, were used to analyze the epidemiological characteristics of the COVID-19 outbreak in Shijiazhuang. The maximum likelihood method and Hamiltonian Monte Carlo method were used to estimate the serial interval and incubation period, respectively. The impact of incubation period and different interventions were simulated using a well-fitted SEIR+q model. Results From 2 January to 14 February 2021, there were 869 patients with symptomatic COVID-19 in Shijiazhuang, and most cases (89.6%) were confirmed before 20 January. Overall, 40.2% of the cases were male, 16.3% were aged 0 to 19 years, and 21.9% were initially diagnosed as asymptomatic but then became symptomatic. The estimated incubation period was 11.6 days (95% CI 10.6, 12.7 days) and the estimated serial interval was 6.6 days (0.025th, 0.975th: 0.6, 20.0 days). The results of the SEIR+q model indicated that a longer incubation period led to a longer epidemic period. If the comprehensive quarantine measures were reduced by 10%, then the nucleic acid testing would need to increase by 20% or more to minimize the cumulative number of cases. Conclusions Incubation period was longer than serial interval suggested that more secondary transmission may occur before symptoms onset. The long incubation period made it necessary to extend the isolation period to control the outbreak. Timely contact tracing and implementation of a centralized quarantine quickly contained this epidemic in Shijiazhuang. Large-scale nucleic acid testing also helped to identify cases and reduce virus transmission.

scholarly journals The seasonal reproduction number of dengue fever impacts of climate on transmission

2016 ◽  
Author(s):  
Sittisede Polwiang
Keyword(s):  
Low Temperature ◽  
Dengue Fever ◽  
Rainy Season ◽  
Reproduction Number ◽  
Dengue Infection ◽  
Viral Disease ◽  
Seasonal Reproduction ◽  
Epidemic Period ◽  
Very High ◽  
Very Low Temperature

Background: The dengue fever is a mosquito-borne viral disease and a regular epidemic in Thailand. The peak of the dengue epidemic period is around June to August during the rainy season. It is believed that the climate is an important factor for dengue transmission.Method: A mathematical model for vector-host infectious disease was used to calculate the impacts of climate to the transmission of dengue virus. In this study, the data of climate and dengue fever cases were derived from Chiang Mai during 2008-2015, Thailand. The value of seasonal reproduction number was calculated to evaluate the potential, severity and persistence of dengue infection.Results: The mosquito population was increasing exponentially from the start of the rainy season in early May and reached its the peak in late June. The simulations suggest that the greatest potential for the dengue transmission occurs when the temperature is 28.9ºC. The seasonal reproduction numbers were larger than one from late March to end of August and reaching the peak in June. The highest incidences occurred in August due to the delay of transmission humans-mosquito-humans. Increasing mean temperature by 1.2ºC, the number of incidences increases 43.7%. However, a very high or very low temperature reduces the number of infection.Discussion and Conclusion: The results show that the dengue infection depends on the seasonal variation of the climate. The rainfall provides places for the mosquitoes to lay eggs and develop to adult stage. The temperature plays an important role in the life cycle and behavior of the mosquitoes. A very high or very low temperature reduces the risk of the dengue infection.

scholarly journals Meta-analysis of the SARS-CoV-2 serial interval and the impact of parameter uncertainty on the COVID-19 reproduction number

2020 ◽  
Author(s):  
Robert Challen ◽  
Ellen Brooks-Pollock ◽  
Krasimira Tsaneva-Atanasova ◽  
Leon Danon
Keyword(s):  
Incubation Period ◽  
Time Delays ◽  
Hospital Admissions ◽  
Reproduction Number ◽  
Meta Analysis ◽  
Model Parameters ◽  
Small Data ◽  
Generation Interval ◽  
Serial Interval ◽  
The Impact

AbstractThe serial interval of an infectious disease, commonly interpreted as the time between onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early COVID-19 data. In this paper we estimate these key quantities in the context of COVID-19 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean 5.6 (95% CrI 5.1–6.2) and SD 4.2 (95% CrI 3.9–4.6) days (empirical distribution), the generation interval with a mean 4.8 (95% CrI 4.3–5.41) and SD 1.7 (95% CrI 1.0–2.6) days (fitted gamma distribution), and the incubation period with a mean 5.5 (95% CrI 5.1–5.8) and SD 4.9 (95% CrI 4.5–5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID-19 transmission.

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