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1.
Euro Surveill ; 27(45)2022 11.
Article in English | MEDLINE | ID: covidwho-2117835

ABSTRACT

BackgroundThe SARS-CoV-2 variant of concern Omicron was first detected in Italy in November 2021.AimTo comprehensively describe Omicron spread in Italy in the 2 subsequent months and its impact on the overall SARS-CoV-2 circulation at population level.MethodsWe analyse data from four genomic surveys conducted across the country between December 2021 and January 2022. Combining genomic sequencing results with epidemiological records collated by the National Integrated Surveillance System, the Omicron reproductive number and exponential growth rate are estimated, as well as SARS-CoV-2 transmissibility.ResultsOmicron became dominant in Italy less than 1 month after its first detection, representing on 3 January 76.9-80.2% of notified SARS-CoV-2 infections, with a doubling time of 2.7-3.3 days. As of 17 January 2022, Delta variant represented < 6% of cases. During the Omicron expansion in December 2021, the estimated mean net reproduction numbers respectively rose from 1.15 to a maximum of 1.83 for symptomatic cases and from 1.14 to 1.36 for hospitalised cases, while remaining relatively stable, between 0.93 and 1.21, for cases needing intensive care. Despite a reduction in relative proportion, Delta infections increased in absolute terms throughout December contributing to an increase in hospitalisations. A significant reproduction numbers' decline was found after mid-January, with average estimates dropping below 1 between 10 and 16 January 2022.ConclusionEstimates suggest a marked growth advantage of Omicron compared with Delta variant, but lower disease severity at population level possibly due to residual immunity against severe outcomes acquired from vaccination and prior infection.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , COVID-19/epidemiology , Vaccination , Base Sequence
2.
Spatial and Spatio-temporal Epidemiology ; : 100539, 2022.
Article in English | ScienceDirect | ID: covidwho-2042149

ABSTRACT

Background: Many questions remain unanswered about how SARS-CoV-2 transmission is influenced by aspects of the economy, environment, and health. A better understanding of how these factors interact can help us to design early health prevention and control strategies, and develop better predictive models for public health risk management of SARS-CoV-2. This study examines the associations between COVID-19 epidemic growth and macro-level determinants of transmission such as demographic factors, socio-economic factors, climate and population health, during the first wave of outbreaks in the United States. Methods: A spatial–temporal data-set was created from a variety of relevant data sources. A unique data-driven study design was implemented to assess the relationship between COVID-19 case and death epidemic doubling times and explanatory variables using a Generalized Additive Model (GAM). Results: The main factors associated with case doubling times are higher population density, home overcrowding, manufacturing, and recreation industries. Poverty was also an important predictor of faster epidemic growth perhaps because of factors associated with in-work poverty-related conditions, although poverty is also a predictor of poor population health which is likely driving case and death reporting. Air pollution and diabetes were other important drivers of case reporting. Warmer temperatures are associated with slower epidemic growth, which is most likely explained by human behaviors associated with warmer locations i.e. ventilating homes and workplaces. and socializing outdoors. The main factors associated with death doubling times were population density, poverty older age, diabetes, and air pollution. Temperature was also slightly significant slowing death doubling times. Conclusions: Such findings help underpin current understanding of the disease epidemiology and also supports current policy and advice recommending ventilation of homes, work-spaces, and schools, along with social distancing and mask-wearing. Given the strong associations between doubling times and the stringency index, it is likely that those states that responded to the virus more quickly by implementing a range of measures such as school closing, workplace closing, restrictions on gatherings, close public transport, restrictions on internal movement, international travel controls, and public information campaigns, did have some success slowing the spread of the virus.

3.
Pakistan Journal of Medical Sciences Quarterly ; 38(5):1228, 2022.
Article in English | ProQuest Central | ID: covidwho-1918831

ABSTRACT

Background and Objectives: Owing to high proliferation rate, multipotency and self-renewal capability, dental pulp stem cells (DPSC) and stem cells from human exfoliated teeth (SHED) have become stem cell source of choice for cell based regenerative therapies. We aimed to compare DPSC and SHED as stem cell sources with a future use in regeneration of calcified tissue. Methods: Explant derived human DPSC (n=9) and SHED (n=1) were cryopreserved, thawed and expanded for analysis of population doubling time, colony forming unit assay and efficiency. A growth curve was plotted to determine population doubling time, while colony forming numbers and efficiency was determined at plating cell densities of 5.6, 11.1 and 22.2 / cm2. The isolated cells were characterized for the presence of stem cell markers by immunophenotyping and immunofluorescence staining, and tri-lineage differentiation. Statistical analysis was performed by Pearson correlation, Exponential regression and two way Anova with Tukey test at p<0.05. Results: DPSC and SHED exhibited spindle shaped fibroblast like morphology. SHED was found superior than DPSC in terms of proliferation and colony forming efficiency. Immunophenotypes showed that DPSC contain 62.6±26.3 %, 90.9±14.8% and 19.8±0.1%, while SHED contain 90.5%, 97.7% and 0.1% positive cells for CD90, CD73 and CD105. DPSC were strongly positive for vimentin, CD29, CD73, while reactivity was moderate to weak against CD44 and CD90. SHED expressed vimentin, CD29, CD105, CD90 and CD44. Both were negative for CD45. Upon induction, both cell types differentiated into bone, fat and cartilage like cells. Conclusion: Cultured DPSC and SHED were proliferative and exhibited self-renewal property. Both DPSC and SHED expressed stem cell markers and were able to differentiate into bone, fat and cartilage like cells. Thus, these could be a suitable stem cell sources for cell based regenerative therapies.

4.
5th International Conference on Smart Computing and Informatics, SCI 2021 ; 283:71-84, 2022.
Article in English | Scopus | ID: covidwho-1899056

ABSTRACT

The novel coronavirus (COVID-19) incidence in India is currently experiencing exponential rise with apparent spatial variation in growth rate and doubling time. We classify the states into five clusters with low- to high-risk category and identify how the different states moved from one cluster to the other since the onset of the first case on January 30th, 2020, till the end of November 30th, 2020. Result clearly shows the impact of the lockdown and the unlock phases in the changing formation of the clusters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

5.
Journal of Scientometric Research ; 11(1):47-54, 2022.
Article in English | Web of Science | ID: covidwho-1897066

ABSTRACT

This study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms. The PubMed, ScienceDirect, and ResearchGate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed "all articles" and ScienceDirect published articles. Analyzed databases were (1A) PubMed (01/12/2019-12/06/2020) "all_articles" (16) PubMed Review articles) and (1C) PubMed Clinical Trials (2) ScienceDirect all publications (01/12/2019- 25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020 - 30/4/2020). Total publications in the observation period for PubMed, ScienceDirect, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, ScienceDirect and RG were 70.0 +/- 128.6, 77.6 +/- 125.3 and 255.6 +/- 205.8 respectively. PubMed shows an avalanche in the number of publications around May 10, the number of publications jumped from 6.0 +/- 8.4/day to 282.5 +/- 110.3/ day. The average doubling time for PubMed, ScienceDirect, and RG was 10.3 +/- 4 days, 20.6 days, and 2.3 +/- 2.0 days respectively. The average number of publications per author for PubMed, ScienceDirect, and RG was 1.2 +/- 1.4, 1.3 +/- 0.9, and 1.1 +/- 0.4 respectively. Subgroup analysis, PubMed review articles mean review <0 vertical bar 17 +/- 17 vertical bar 77> days: and reducing at a rate of -0.21 days (count)/day. The number of publications related to the COVID-19 until now is huge and growing very fast with time. It is essential to rationalize and limit the publications.

6.
Computers, Materials and Continua ; 72(1):833-849, 2022.
Article in English | Scopus | ID: covidwho-1732652

ABSTRACT

COVID-19 has become a pandemic, with cases all over the world, with widespread disruption in some countries, such as Italy, US, India, South Korea, and Japan. Early and reliable detection of COVID-19 is mandatory to control the spread of infection. Moreover, prediction of COVID-19 spread in near future is also crucial to better plan for the disease control. For this purpose, we proposed a robust framework for the analysis, prediction, and detection of COVID-19.We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world. The estimates, analysis and predictions are based on the data gathered fromJohns Hopkins Center during the time span of April 21 to June 27, 2020. We use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different continents. The predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well justified. The parameters of Gaussian distribution, i.e., maximumtime and width, are determined through a statistical x2-fit for the purpose of doubling times after April 21, 2020. For COVID-19 detection, we proposed a novel method based on the Histogram of Oriented Gradients (HOG) and CNN in multi-class classification scenario i.e., Normal, COVID-19, viral pneumonia etc. Experimental results show the effectiveness of our framework for reliable prediction and detection of COVID-19. © 2022 Tech Science Press. All rights reserved.

8.
Communication in Biomathematical Sciences ; 4(2):93-105, 2021.
Article in English | Scopus | ID: covidwho-1706075

ABSTRACT

In this paper, a mathematical model for COVID-19 pandemic that spreads through horizontal transmission in the presence of exposed immigrants is studied. The model has equilibrium points, notably, COVID-19-free equilibrium and COVID-19-endemic equilibrium points. The model exhibits a basic reproduction number, R0 which determines the elimination and persistence of the disease. It was found that when R0 < 1, then the equilibrium becomes locally asymptotically stable and endemic equilibrium does not exists. However, when R0 > 1, the equilibrium is found to be stable globally. This implies that continuous mixing of exposed immigrants with the susceptible population will make the eradication of COVID-19 difficult and endemic in the community. The system is also proved qualitatively to experience transcritical bifurcation close to the COVID-19-free equilibrium at the point R0 = 1. Numerically, the model is used to investigate the impact of certain other relevant parameters on the spread of COVID-19 and how to curtail their effect. © 2021 Published by Indonesian Biomathematical Society.

9.
Front Public Health ; 9: 754696, 2021.
Article in English | MEDLINE | ID: covidwho-1575228

ABSTRACT

Background: Attempts to quantify effect sizes of non-pharmaceutical interventions (NPI) to control COVID-19 in the US have not accounted for heterogeneity in social or environmental factors that may influence NPI effectiveness. This study quantifies national and sub-national effect sizes of NPIs during the early months of the pandemic in the US. Methods: Daily county-level COVID-19 cases and deaths during the first wave (January 2020 through phased removal of interventions) were obtained. County-level cases, doubling times, and death rates were compared to four increasingly restrictive NPI levels. Socio-demographic, climate and mobility factors were analyzed to explain and evaluate NPI heterogeneity, with mobility used to approximate NPI compliance. Analyses were conducted separately for the US and for each Census regions (Pacific, Mountain, east/West North Central, East/West South Central, South Atlantic, Middle Atlantic and New England). A stepped-wedge cluster-randomized trial analysis was used, leveraging the phased implementation of policies. Results: Aggressive (level 4) NPIs were associated with slower COVID-19 propagation, particularly in high compliance counties. Longer duration of level 4 NPIs was associated with lower case rates (log beta -0.028, 95% CI -0.04 to -0.02) and longer doubling times (log beta 0.02, 95% CI 0.01-0.03). Effects varied by Census region, for example, level 4 effects on doubling time in Pacific states were opposite to those in Middle Atlantic and New England states. NPI heterogeneity can be explained by differential timing of policy initiation and by variable socio-demographic county characteristics that predict compliance, particularly poverty and racial/ethnic population. Climate exhibits relatively consistent relationships across Census regions, for example, higher minimum temperature and specific humidity were associated with lower doubling times and higher death rates for this period of analysis in South Central, South Atlantic, Middle Atlantic, and New England states. Conclusion and Relevance: Heterogeneity exists in both the effectiveness of NPIs across US Census regions and policy compliance. This county-level variability indicates that control strategies are best designed at community-levels where policies can be tuned based on knowledge of local disparities and compliance with public health ordinances.


Subject(s)
COVID-19 , RNA, Viral , Humans , Pandemics , Policy , SARS-CoV-2 , United States/epidemiology
10.
Infect Dis Model ; 6: 1159-1172, 2021.
Article in English | MEDLINE | ID: covidwho-1466377

ABSTRACT

While there are many online data dashboards on COVID-19, there are few analytics available to the public and non-epidemiologists to help them gain a deeper insight into the COVID-19 pandemic and evaluate the effectiveness of social intervention measures. To address the issue, this study describes the methods underlying the development of a real-time, data-driven online Epidemic Calculator for tracking COVID-19 growth parameters. From publicly available infection case and death data, the calculator is used to estimate the effective reproduction number, final epidemic size, and death toll. As a case study, we analyzed the results for Singapore during the "Circuit Breaker" period from April 7, 2020 to the end of May 2020. The calculator shows that the stringent measures imposed have an immediate effect of rapidly slowing down the spread of the coronavirus. After about two weeks, the effective reproduction number reduced to about 1.0. Since then, the number has been fluctuating around 1.0 for more than a month. The COVID-19 Epidemic Calculator is available in the form of an online Google Sheet and the results are presented as Tableau Public dashboards at www.cv19.one. By making the calculator readily accessible online, the public can have a tool to assess the effectiveness of measures to control the pandemic meaningfully.

11.
Comput Methods Biomech Biomed Engin ; 25(6): 668-674, 2022 May.
Article in English | MEDLINE | ID: covidwho-1416050

ABSTRACT

In this paper, we have calculated the basic reproduction number (R0) and doubling time (Td) for the novel Coronavirus disease 2019 (COVID-19). The calculation is performed for March 2020 from the data provided by worldometer. We have investigated the data for Germany and Bangladesh. The calculation of R0 is performed based on SIR model. The parameter Td is estimated based on the new cases of each day. Since Td and R0 in use to judge the lockdowns and other measures to prevent spreading of the virus, we have provided simple approximation of both parameters.


Subject(s)
COVID-19 , Basic Reproduction Number , Communicable Disease Control , Humans , Reproduction , SARS-CoV-2
12.
Phys Biol ; 18(4)2021 06 21.
Article in English | MEDLINE | ID: covidwho-1243452

ABSTRACT

While the mathematical laws of uncontrolled epidemic spreading are well known, the statistical physics of coronavirus epidemics with containment measures is currently lacking. The modelling of available data of the first wave of the Covid-19 pandemic in 2020 over 230 days, in different countries representative of different containment policies is relevant to quantify the efficiency of these policies to face the containment of any successive wave. At this aim we have built a 3D phase diagram tracking the simultaneous evolution and the interplay of the doubling time,Td, and the reproductive number,Rtmeasured using the methodological definition used by the Robert Koch Institute. In this expanded parameter space three different main phases,supercritical,criticalandsubcriticalare identified. Moreover, we have found that in thesupercriticalregime withRt> 1 the doubling time is smaller than 40 days. In this phase we have established the power law relation betweenTdand (Rt- 1)-νwith the exponentνdepending on the definition of reproductive number. In thesubcriticalregime whereRt< 1 andTd> 100 days, we have identified arrested metastable phases whereTdis nearly constant.


Subject(s)
COVID-19/epidemiology , SARS-CoV-2/drug effects , Computer Simulation , Humans , Models, Biological , Pandemics , Time Factors
13.
Int J Environ Res Public Health ; 18(7)2021 03 24.
Article in English | MEDLINE | ID: covidwho-1154399

ABSTRACT

Long diagnostic delays (LDDs) may decrease the effectiveness of patient isolation in reducing subsequent transmission of coronavirus disease 2019 (COVID-19). This study aims to investigate the correlation between the proportion of LDD of COVID-19 patients with unknown transmission routes and the subsequent doubling time. LDD was defined as the duration between COVID-19 symptom onset and confirmation ≥6 days. We investigated the geographic correlation between the LDD proportion among 369 confirmed COVID-19 patients with symptom onset between the 9th and 11th week and the subsequent doubling time for 717 patients in the 12th-13th week among the six prefectures. The doubling time on March 29 (the end of the 13th week) ranged from 4.67 days in Chiba to 22.2 days in Aichi. Using a Pearson's product-moment correlation (p-value = 0.00182) and multiple regression analyses that were adjusted for sex and age (correlation coefficient -0.729, 95% confidence interval: -0.923--0.535, p-value = 0.0179), the proportion of LDD for unknown exposure patients was correlated inversely with the base 10 logarithm of the subsequent doubling time. The LDD for unknown exposure patients was correlated significantly and inversely with the subsequent doubling time.


Subject(s)
COVID-19 , Delayed Diagnosis , Humans , Japan/epidemiology , Patient Isolation , SARS-CoV-2
14.
Epidemiologia (Basel) ; 2(1): 95-113, 2021 Mar 11.
Article in English | MEDLINE | ID: covidwho-1125891

ABSTRACT

To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models and used the local Moran's I to test for spatial autocorrelation and clustering. Four hundred prefectures (~98% population) had at least one COVID-19 case and 39 prefectures had zero cases by 24 February 2020. Excluding Wuhan and those prefectures where there was only one case or none, 76 (17.3% of 439) prefectures had an arithmetic mean of the epidemic doubling time <2 d. Low-population prefectures had a higher per capita cumulative incidence than high-population prefectures during the study period. An increase in population size was associated with a very small reduction in the mean doubling time (-0.012, 95% CI, -0.017, -0.006) where the cumulative case count doubled ≥3 times. Spatial analysis revealed high case count clusters in Hubei and Heilongjiang and fast epidemic growth in several metropolitan areas by mid-February 2020. Prefectures in Hubei and neighboring provinces and several metropolitan areas in coastal and northeastern China experienced rapid growth with cumulative case count doubling multiple times with a small mean doubling time.

15.
Int J Environ Res Public Health ; 18(5)2021 02 28.
Article in English | MEDLINE | ID: covidwho-1121211

ABSTRACT

Background. The doubling time is the best indicator of the course of the current COVID-19 pandemic. The aim of the present investigation was to determine the impact of policies and several sociodemographic factors on the COVID-19 doubling time in Mexico. Methods. A retrospective longitudinal study was carried out across March-August, 2020. Policies issued by each of the 32 Mexican states during each week of this period were classified according to the University of Oxford Coronavirus Government Response Tracker (OxCGRT), and the doubling time of COVID-19 cases was calculated. Additionally, variables such as population size and density, poverty and mobility were included. A panel data model was applied to measure the effect of these variables on doubling time. Results. States with larger population sizes issued a larger number of policies. Delay in the issuance of policies was associated with accelerated propagation. The policy index (coefficient 0.60, p < 0.01) and the income per capita (coefficient 3.36, p < 0.01) had a positive effect on doubling time; by contrast, the population density (coefficient -0.012, p < 0.05), the mobility in parks (coefficient -1.10, p < 0.01) and the residential mobility (coefficient -4.14, p < 0.01) had a negative effect. Conclusions. Health policies had an effect on slowing the pandemic's propagation, but population density and mobility played a fundamental role. Therefore, it is necessary to implement policies that consider these variables.


Subject(s)
COVID-19/epidemiology , Health Policy , Pandemics , Socioeconomic Factors , COVID-19/transmission , Humans , Longitudinal Studies , Mexico/epidemiology , Population Density , Retrospective Studies
16.
Environ Sci Pollut Res Int ; 2021 Feb 26.
Article in English | MEDLINE | ID: covidwho-1103508

ABSTRACT

Northern Italy was the most affected by CoViD-19 compared to other Italian areas and comprises zones where air pollutants concentration was higher than in the rest of Italy. The aim of the research is to determine if particulate matter (PM) has been the primary cause of the high CoViD-19 spread rapidity in some areas of Northern Italy. Data of PM for all the 41 studied cities were collected from the local environmental protection agencies. To compare air quality data with epidemiological data, a statistical analysis was conducted identifying the correlation matrices of Pearson and Spearman, considering also the possible incubation period of the disease. Moreover, a model for the evaluation of the epidemic risk, already proposed in literature, was used to evaluate a possible influence of PM on CoViD-19 spread rapidity. The results exclude that PM alone was the primary cause of the high CoVid-19 spread rapidity in some areas of Northern Italy. Further developments are necessary for a better comprehension of the influence of atmospheric pollution parameters on the rapidity of spread of the virus SARS-CoV-2, since a synergistic action with other factors (such as meteorological, socio-economic and cultural factors) could not be excluded by the present study.

17.
Soc Sci Med ; 270: 113645, 2021 02.
Article in English | MEDLINE | ID: covidwho-989251

ABSTRACT

This paper employs Autoregressive Integrated Moving Average (ARIMA) modelling and doubling time to assess the effect of lockdown and reopening on the active COVID-19 cases (ACC) based on a sample from 29 February to July 3, 2020. Two models are estimated: one with a sample covering post-lockdown period only and another spanning both post-lockdown and post-reopening periods. The first model reveals that the lockdown caused an immediate fall in the daily growth rate of the ACC by 14.30% and 33.26% fall in the long run. The parameters of the second model show that the lockdown had an impact effect of 8.56% and steady state effect of 20.88% reduction in the growth rate of the ACC. The effect of reopening on the ACC is insignificant. However, the doubling time of the ACC has increased after reopening. The study warns against complete reopening until sufficient post-reopening data series is available for exact estimation. The findings in this study can be useful in determining the hospitalisation needs and effectiveness of similar health-related policies.


Subject(s)
COVID-19 , Quarantine , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Models, Statistical , Nigeria/epidemiology , Quarantine/statistics & numerical data
18.
Int J Infect Dis ; 103: 389-394, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-957128

ABSTRACT

BACKGROUND: Migrant worker dormitories-residential complexes where 10-24 workers share living spaces-account for the majority of cases of SARS-CoV-2 infection in Singapore. To prevent overspill of transmission to the wider population, starting in early April 2020, residents were confined to their dormitories while measures were put in place to arrest the spread of infection. This descriptive study presents epidemiological data for a population of more than 60 000 migrant workers living in two barracks-style and four apartment-style dormitories located in western Singapore from April 3 to June 10, 2020. METHODS: Our report draws from data obtained over the first 50 days of outbreak management in order to describe SARS-CoV-2 transmission in high-density housing environments. Cumulative counts of SARS-CoV-2 cases and numbers of housing units affected were analyzed to report the harmonic means of harmonic means of doubling times and their 95% confidence intervals (CI). RESULTS: Multiple transmission peaks were identified involving at least 5467 cases of SARS-CoV-2 infection across six dormitories. Our geospatial heat maps gave an early indication of outbreak severity in affected buildings. We found that the number of cases of SARS-CoV-2 infection doubled every 1.56 days (95% CI 1.29-1.96) in barracks-style buildings. The corresponding doubling time for apartment-style buildings was 2.65 days (95% CI 2.01-3.87). CONCLUSIONS: Geospatial epidemiology was useful in shaping outbreak management strategies in dormitories. Our results indicate that building design plays an integral role in transmission and should be considered in the prevention of future outbreaks.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Housing , Transients and Migrants , Adult , COVID-19/prevention & control , Disease Outbreaks/prevention & control , Humans , Male , Middle Aged , SARS-CoV-2 , Singapore/epidemiology , Spatio-Temporal Analysis , Young Adult
19.
J Family Med Prim Care ; 9(9): 4507-4511, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-914640

ABSTRACT

BACKGROUND: Lockdown effectively can only result in relative freezing of populations that is expected to slow down the disease spread rather than zeroing it. Flattening of epidemic curve Current analysis was carried out to observe a pattern in the rise of CoVID-19 cases along with concurrent announcements of strategies to control the spread of disease. MATERIAL AND METHODS: Data in from of daily number of cases and issued notifications were studied from the official website of Government of India from 30/01/2020 to 03/05/2020. Qualitative assessment with thematic analysis was carried out for notifications issued by the government. The fit to data on cumulative cases was observed with R2 and checked for linearity, logarithmic, polynomial, and exponential growth. Daily growth fraction (Gt) was calculated based on the difference between current and previous number of cases, thereafter daily doubling time (Td(t)) was estimated. RESULTS: Daily reported cases were entered and cumulative growth of cases observed with a polynomial increasing pattern (third-order) with better fit (R2: 0.999). Total 108 notifications were issued, and as compared to phase-0 and 1 (87.0%), few (12.9%) notifications were issued in phase-2 of study period. As compared to phase-0 and 1, rising trend of cumulative cases and Td(t) was high in phase-2. CONCLUSION: Across phases of lockdown along with a rising trend of COVID-19 cases, the country has managed to increase the doubling time of cases with an effort to flatten the epidemic curve.

20.
J Data Sci ; 18(3): 536-549, 2020 07.
Article in English | MEDLINE | ID: covidwho-890632

ABSTRACT

As the COVID-19 pandemic has strongly disrupted people's daily work and life, a great amount of scientific research has been conducted to understand the key characteristics of this new epidemic. In this manuscript, we focus on four crucial epidemic metrics with regard to the COVID-19, namely the basic reproduction number, the incubation period, the serial interval and the epidemic doubling time. We collect relevant studies based on the COVID-19 data in China and conduct a meta-analysis to obtain pooled estimates on the four metrics. From the summary results, we conclude that the COVID-19 has stronger transmissibility than SARS, implying that stringent public health strategies are necessary.

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