Prediction of the time evolution of the COVID-19 disease in Guadeloupe with a stochastic evolutionary model

Abstract

medRxiv preprint Predictions on the time-evolution of the number of severe and critical cases of COVID-19 patients in Guadeloupe are presented. A stochastic model is purposely developed to explicitly account for the entire population ( 400000 inhabitants) of Guadeloupe. The available data for Guadeloupe are analysed and combined with general characteristics of the COVID-19 to constrain the parameters of the model. The time-evolution of the number of cases follows the well-known exponential-like model observed at the very beginning of a pandemic outbreak. The exponential growth of the number of infected individuals is controlled by the so-called basic reproductive number, R 0 , defined as the likely number of additional cases generated by a single infectious case during its infectious period T I . Because of the rather long duration of infectious period ( 14 days) a high rate of contamination is sustained during several weeks after the beginning of the containment period. This may constitute a source of discouragement for people restrained to respect strict containment rules. It is then unlikely that, during the containment period, R 0 falls to zero. Fortunately, our models shows that the containment effects are not much sensitive to the exact value of R 0 provided we have R 0 < 0.6. For such conditions, we show that the number of severe and critical cases is highly tempered about 4 to 6 weeks after the beginning of the containment. Also, the maximum number of critical cases (i.e. the cases that may exceed the hospital's intensive care capacity) remains near 30 when R 0 < 0.6. For a larger R 0 = 0.8 a slower decrease of the number of critical cases occurs, leading to a larger number of deceased patients. This last example illustrates the great importance to maintain an as low as possible R 0 during and after the containment period. The rather long delay between the beginning of the containment and the appearance of the slowing-down of the rate of contamination puts a particular strength on the communication and sanitary education of people. To be mostly efficient, this communication must be done by a locally recognised medical staff. We believe that this point is a crucial matter of success. COVID-19 | Time-evolution | Guadeloupe | SAMU | Critical care | Monte Carlo ModelCorrespondence: meriem.allali@chu-guadeloupe.fr A. Description of the model structure. The stochastic model used in the present study falls in the class of exact stochastic Monte Carlo models (2). In such models, the entire population exposed to an infection is represented by a network where each node represents a person. This type of models offers very large possibilities to design epidemic processes that agree with the medical knowledge of the virus dissemination and its possible pathological issues. A set of stochastic rules determines the evolution of each node at each time iteration. The infection simulation process begins by choosing a given number of nodes initialised as "infected" to put the pathogen in the network. Other initial condition...

Monitoring the post-containment COVID-19 crisis in Guadeloupe: Early-warning signals of destabilisation through bootstrapped probability density functions

“…The efficiency of the containment is variable from one region to another, depending on the sanitary situation at the beginning of the containment. As shown in our previous study (4), the case of Guadeloupe was particularly favourable to a good control of the epidemic spread due to several factors: i) the small number of infected persons at the beginning of the containment, ii) a good and coherent communication by the sanitary and administrative authorities, iii) a good respect of the social distancing rules by a vast majority of the population, iv) a tight control of the incoming passengers by either airport or ship.…”

“…We now address the main question of the present study, namely the online analysis of a simulated destabilisation of the COVID-19 spread control. We use the stochastic computer code developed in (4, 5) to simulate a situation where we start from a stable convergent solution to cross the critical boundary in the solution domain (Fig. 1.…”

“…The aim of this monitoring is to control that the situation remains in the domain of stable solutions as described by Allali et al (5) and that it does not evolves dangerously toward the critical boundary. In the next Section, we present some simulations performed with the stochastic model developed in Allali et al (4). These simulations are performed with a time-varying R 0 in order to cross the critical boundary from a stable solution to an unstable one.…”

Modeling and forecasting hospitalized mortality rate of COVID-19 according to delay effect of daily hospitalization

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