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1.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.12.18.21268024

ABSTRACT

This article aims to study the COVID-19 data for New York City. We use both the daily number of second does vaccination and the daily number of reported cases for New York City. This article provides a method to combine an epidemic model and such data. We explore the influence of vaccine efficacy on our results.


Subject(s)
COVID-19
2.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.06.16.21259019

ABSTRACT

Background: The COVID-19 epidemic, which started in late December 2019 and rapidly spread throughout the world, was accompanied by an unprecedented release of reported case data. Our objective is to propose a fresh look at this data by coupling a phenomenological description to the epidemiological dynamics. Methods: We use a phenomenological model to describe and regularize the data. This model can be matched by a single mathematical model reproducing the epidemiological dynamics with a time-dependent transmission rate. We provide a method to compute this transmission rate and reconstruct the changes in the social interactions between people as well as changes in host-pathogen interactions. This method is applied to the cumulative case data of 8 different geographic areas. Findings: We reconstruct the transmission rate from the data, therefore we are in position to understand the contribution of the dynamical effects of social interactions (contacts between individuals) and the contribution of the dynamics of the epidemic. We deduce from the comparison of several instantaneous reproduction numbers that the social effects are the most important in the dynamic of COVID-19. We obtain an instantaneous reproduction number that stays below $3.5$ from early beginning of the epidemic. Conclusion: The instantaneous reproduction number staying below $3.5$ implies that it is sufficient to vaccinate $71\%$ of the population in each state or country considered in our study. Therefore assuming the vaccines will remain efficient against the new variants, and to be more confident it is sufficient to vaccinate $75-80\%$ to get rid of COVID-19 in each state or country. Funding: This research was funded by the Agence Nationale de la Recherche in France (Project name: MPCUII (PM) and (QG))


Subject(s)
COVID-19
3.
ssrn; 2021.
Preprint in English | PREPRINT-SSRN | ID: ppzbmed-10.2139.ssrn.3784589

ABSTRACT

With the spread of COVID-19 across the world, a large amount of data on reported cases has become available. We are studying here a potential bias induced by the daily number of tests which may be insufficient or vary over time. Indeed, tests are hard to produce at the early stage of the epidemic and can therefore be a limiting factor in the detection of cases. Such a limitation may have a strong impact on the reported cases data. Indeed, some cases may be missing from the official count because the number of tests was not sufficient on a given day. In this work, we propose a new differential equation epidemic model which uses the daily number of tests as an input. We obtain a good agreement between the model simulations and the reported cases data coming from the state of New York. We also explore the relationship between the dynamic of the number of tests and the dynamics of the cases. We obtain a good match between the data and the outcome of the model. Finally, by multiplying the number of tests by 2, 5, 10, and 100 we explore the consequences for the number of reported cases.


Subject(s)
COVID-19
4.
ssrn; 2021.
Preprint in English | PREPRINT-SSRN | ID: ppzbmed-10.2139.ssrn.3784137

ABSTRACT

The article is devoted to the parameters identification in the SI model. We consider several methods, starting with an exponential fit of the early cumulative data of SARS-CoV2 in mainland China. The present methodology provides a way to compute the parameters at the early stage of the epidemic. Next, we establish an identifiability result. Then we use the Bernoulli-Verhulst model as a phenomenological model to fit the data and derive some results on the parameters identification. The last part of the paper is devoted to some numerical algorithms to fit a daily piecewise constant rate of transmission.


Subject(s)
COVID-19
5.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.02.10.21251500

Subject(s)
COVID-19
6.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.10.19.20214528

ABSTRACT

The article is devoted to the parameters identification in the SI model. We consider several methods, starting with an exponential fit of the early cumulative data of Sars-CoV2 in mainland China. The present methodology provides a way to compute the parameters at the early stage of the epidemic. Next, we establish an identifiability result. Then we use the Bernoulli-Verhulst model as a phenomenological model to fit the data and derive some results on the parameters identification. The last part of the paper is devoted to some numerical algorithms to fit a daily piecewise constant rate of transmission.


Subject(s)
COVID-19
8.
ssrn; 2020.
Preprint in English | PREPRINT-SSRN | ID: ppzbmed-10.2139.ssrn.3699470

ABSTRACT

We investigate the age structured data for the COVID-19 outbreak in Japan. We consider epidemic mathematical model with unreported infectious patient with and without age structure. In particular, we build a new mathematical model which allows to take into account differences in the response of patients to the disease according to their age. This model also allows for a heterogeneous response of the population to the social distancing measures taken by the local government. We fit this model to the observed data and obtain a snapshot of the effective transmissions occurring inside the population at different times, which indicates where and among whom the disease propagates after the start of the public measures.


Subject(s)
COVID-19
9.
researchsquare; 2020.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-73650.v1

ABSTRACT

Background SARS-CoV-2 is a novel virus that appeared in China in November 2019 and spread rapidly. With no vaccine or effective treatment, countries have adopted different mitigation measures to reduce SARS-COV-2 spread with different efficacy.MethodsWe mapped the impact of mitigation measures across different countries. We compared regional SARS-COV-2 population burden via Kruskal-Wallis statistical testing. We analyzed time of adoption of mitigation measures and the impact of PCR testing on mitigation impact. We analyzed the association of climate, health, demographic and economic indicators with mitigation impact via non-parametric correlation tests. We performed mechanistic modelling of to predict short-term SARS-COV-2 case numbers in selected countries. ResultsMany countries showed a reduction of infection rates within one month of implementing mitigation measures. However, we identified a geographic cluster of countries centered on the Arabian Peninsula (AP) that show a high SARS-COV-2 population burden despite early adoption of mitigation measures. We find that higher air pollution levels (p=0.01), higher CO2 emissions (p=0.03) and younger population (p=0.02) were associated with reduced mitigation impact in AP countries. We also show that mechanistic modelling can closely predict confirmed case numbers in the short term.ConclusionsThe impact of mitigation measures varies greatly between countries. Countries with similar profiles as AP countries should adopt more stringent mitigation measures to more rapidly reduce SARS-CoV-2 spread. Specific interventions targeting young people may also be effective in reducing SARS-COV-2 spread.

10.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.05.07.20093807

ABSTRACT

We investigate the age structured data for the COVID-19 outbreak in Japan. We consider epidemic mathematical model with unreported infectious patient with and without age structure. In particular, we build a new mathematical model which allows to take into account differences in the response of patients to the disease according to their age. This model also allows for a heterogeneous response of the population to the social distancing measures taken by the local government. We fit this model to the observed data and obtain a snapshot of the effective transmissions occurring inside the population at different times, which indicates where and among whom the disease propagates after the start of the public measures.


Subject(s)
COVID-19
11.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.04.14.20064824

ABSTRACT

Our main aim is to estimate the end of the first wave epidemic of COVID-19 outbreak in mainland China. We developed mathematical models to predict reasonable bounds on the date of end of the COVID-19 epidemics in mainland China with strong quarantine and testing measures for a sufficiently long time. We used reported data in China from January 20, 2020 to April 9, 2020. We firstly used a deterministic approach to obtain a formula to compute the probability distribution of the extinction date by combining the models and continuous-time Markov processes. Then we present the individual based model (IMB) simulations to compare the result by deterministic approach and show the absolute difference between the estimated cumulative probability distribution computed by simulations and formula. We provide the predictions of the end of the first wave epidemic for different fractions $f$ of asymptomatic infectious that become reported symptomatic infectious.


Subject(s)
COVID-19
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