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...
Using a stochastic epidemic model explicitly considering the entire population of Guadeloupe (1), we explore the domain of solutions presenting an efficient slowing down of the COVID-19 epidemic spread during the post-containment period. The considered model parameters are the basic reproduction number R 0 to simulate the effects of social distancing, the time delay δT Q elapsed between the detection of a symptomatic person and her/his placement in quarantine to suppress her/his contagiousness, and the number Na of asymptomatic people tested positively and isolated. We show that acceptable solutions are obtained for a wide range of parameter values. Thanks to a good control of the initial epidemic spread resulting from an early containment and efficient communication by the sanitary and administrative authorities, the present situation corresponds to a pre-epidemic state. The most safe solutions are a combinations of social distancing, numerous testing to perform a systematic isolation of symptomatic patients and guided detection of asymptomatic people in the entourage of localised symptomatic patients. COVID-19 | Time-evolution | Guadeloupe | SAMU | Critical care | Monte Carlo Model | Spread slowdown | Quarantine | TestingCorrespondence: meriem.allali@chu-guadeloupe.fr
We propose a method to detect early-warning information in relation with subtle changes occurring in the trend of evolution in data time series of the COVID-19 epidemic spread (e.g. daily new cases). The method is simple and easy to implement on laptop computers. It is designed to be able to provide reliable results even with very small amounts of data (i.e. ≈ 10 − 20). The results are given as compact graphics easy to interpret. The data are separated into two subsets: the old data used as control points to statistically define a "trend" and the recent data that are tested to evaluate their conformity with this trend. The trend is characterised by bootstrapping in order to obtain probability density functions of the expected misfit of each data point. The probability densities are used to compute distance matrices where data clusters and outliers are easily visually recognised. In addition to be able to detect very subtle changes in trend, the method is also able to detect outliers. A simulated case is analysed where R0 is slowly augmented (i.e. from 1.5 to 2.0 in 20 days) to pass from a stable damped control of the epidemic spread to an exponentially diverging situation. The method is able to give an early warning signal as soon as the very beginning of the R0 variation. Application to the data of Guadeloupe shows that a small destabilising event occurred in the data near April 30, 2020. This may be due to an increase of R0 ≈ 0.7 around April 13-15, 2020.
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