Real-Time Assessment of Health-Care Requirements During the Zika Virus Epidemic in Martinique.

The spread of Zika virus in the Americas has been associated with a surge in Guillain-Barré syndrome (GBS) cases. Given the severity of GBS, territories affected by Zika virus need to plan health-care resources to manage GBS patients. To inform such planning in Martinique, we analyzed Zika virus surveillance and GBS data from Martinique in real time with a modeling framework that captured dynamics of the Zika virus epidemic, the risk of GBS in Zika virus-infected persons, and the clinical management of GBS cases. We compared our estimates with those from the 2013-2014 Zika virus epidemic in French Polynesia. We were able to predict just a few weeks into the epidemic that, due to lower transmission potential and lower probability of developing GBS following infection in Martinique, the total number of GBS cases in Martinique would be substantially lower than suggested by simple extrapolations from French Polynesia. We correctly predicted that 8 intensive-care beds and 7 ventilators would be sufficient to treat GBS cases. This study showcased the contribution of modeling to inform local health-care planning during an outbreak. Timely studies that estimate the proportion of infected persons that seek care are needed to improve the predictive power of such approaches.

Reporting delays: A widely neglected impact factor in COVID-19 forecasts.

Epidemic forecasts are only as good as the accuracy of epidemic measurements. Is epidemic data, particularly COVID-19 epidemic data, clean, and devoid of noise? The complexity and variability inherent in data collection and reporting suggest otherwise. While we cannot evaluate the integrity of the COVID-19 epidemic data in a holistic fashion, we can assess the data for the presence of reporting delays. In our work, through the analysis of the first COVID-19 wave, we find substantial reporting delays in the published epidemic data. Motivated by the desire to enhance epidemic forecasts, we develop a statistical framework to detect, uncover, and remove reporting delays in the infectious, recovered, and deceased epidemic time series. Using our framework, we expose and analyze reporting delays in eight regions significantly affected by the first COVID-19 wave. Further, we demonstrate that removing reporting delays from epidemic data by using our statistical framework may decrease the error in epidemic forecasts. While our statistical framework can be used in combination with any epidemic forecast method that intakes infectious, recovered, and deceased data, to make a basic assessment, we employed the classical SIRD epidemic model. Our results indicate that the removal of reporting delays from the epidemic data may decrease the forecast error by up to 50%. We anticipate that our framework will be indispensable in the analysis of novel COVID-19 strains and other existing or novel infectious diseases.