The SARS-CoV-2 epidemic outbreak: a review of plausible scenarios of containment and mitigation for Mexico

Manuel Adrian Acuna-Zegarra; Andreu Comas-Garcia; Esteban Hernandez-Vargas; Mario Santana-Cibrian; Jorge X. Velasco-Hernandez

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.28.20046276 doi: medRxiv preprint η = 1/10, ρ = 0.80, ω = 0.01, q 1 = 0.10, q = 0.99. Figure 13 shows the contact rates for the isolated environment. For both rates, the reduction of contact is 90% (q 1 = 0.1), the day of initial isolation is January 26th. We can impose a fast behavioral change because we think that this process lasted a short time (7 days) in Wuhan. Figure 14 presents the case when the 99% of population is isolated. Observe that the peak incidence is approximately 4 935 individuals and it occurs 2 days after isolation began (January 26th, [20] ) Figure 14(b) ). Our simulations show that carrying out an isolation process promotes an earlier decrease in incidence. On the other hand, considering a total population equal to 14 million [21] the prevalence at the peak of the epidemic in Wuhan is around 19 000 cases which is a gross underestimate of the true observed value [6] . A better estimate of the at-risk population in Wuhan would, we believe, improve or estimate. Figure 15 shows a comparison of the estimated cumulative incidence (blue line) versus the data reported by the WHO (red cross) for the Hubei province [20] . Our curves overestimate the data, which may be reasonable due the known under-reporting of cases and to the fact that initially there are fewer cases making it difficult to detect them. Nonetheless we see that when the real epidemic curve begins to stabilize, the difference between both data and model decreases, which can be explained if we accept that, at this stage, there are many more cases and it is easier to identify them. Also under-reporting decreases for the same reason. The black point on the model cumulative incidence (blue curve) indicates the inflection point. The black dots over the red curve mark the range where the inflection point is located in the data.