Impact of data accuracy on the evaluation of COVID-19 mitigation policies

Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia

This research studies the evolution of COVID-19 crude incident rates, effective reproduction number R(t) and their relationship with incidence spatial autocorrelation patterns in the 19 months following the disease outbreak in Catalonia (Spain). A cross-sectional ecological panel design based on n = 371 health-care geographical units is used. Five general outbreaks are described, systematically preceded by generalized values of R(t) > 1 in the two previous weeks. No clear regularities concerning possible initial focus appear when comparing waves. As for autocorrelation, we identify a wave's baseline pattern in which global Moran's I increases rapidly in the first weeks of the outbreak to descend later. However, some waves significantly depart from the baseline. In the simulations, both baseline pattern and departures can be reproduced when measures aimed at reducing mobility and virus transmissibility are introduced. Spatial autocorrelation is inherently contingent on the outbreak phase and is also substantially modified by external interventions affecting human behavior.

Participatory surveillance for COVID-19 trends detection in Brazil: Cross-section study

BACKGROUND

The ongoing COVID-19 pandemic has emphasized the necessity of a well-functioning surveillance system to detect and mitigate disease outbreaks. Traditional surveillance (TS) usually relies on healthcare providers and generally suffers from reporting lags that prevent immediate response plans. Participatory surveillance (PS), an innovative digital approach whereby individuals voluntarily monitor and report on their own health status via Web-based surveys, has emerged in the past decade to complement traditional data collections approaches.

OBJECTIVE

This study compares novel PS data on COVID-19 infection rates across nine Brazilian cities with official TS data to examine the opportunities and challenges of using the former, and the potential advantages of combining the two approaches.

METHODS

The traditional surveillance data for Brazil, prospectively called the TS data, is publicly accessible on GitHub. The participatory surveillance data was collected through the Brazil Sem Corona - a Colab platform. To gather information on an individual's health status, each participant was asked to fill out a daily questionnaire into the Colab app on symptoms as well as exposure.

RESULTS

We find that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we document a significant trend correlation between lagged PS data and TS infection rates, suggesting that the former could be used for early detection. In our data, forecasting models integrating both approaches increased accuracy up to 3% relative to a 14-day forecast horizon model based exclusively on TS data. Furthermore, we show that the PS data captures a population that significantly differs from the traditional observation.

CONCLUSIONS

In the traditional system, the new recorded COVID-19 cases per day are aggregated based on positive lab-confirmed tests. In contrast, the PS data shows a significant share of reports categorized as potential COVID-19 case that are not lab-confirmed. Quantifying the economic value of a PS system implementation remains hard. But scarce public funds as well as persisting constraints to the TS system motivate for a PS system, making it an important avenue for future research. The decision to set up a PS system requires careful evaluation of its expected benefits, relative to the costs of setting up platforms and incentivizing engagement to increase both coverage and consistent reporting over time. The ability to compute such economic trade-offs might be key to have PS become a more integral part of policy toolkits moving forward. These results corroborate previous studies when it comes to the benefits of an integrated and comprehensive surveillance system, but also shed lights on its limitations, and on the need for additional research to improve future implementations of PS platforms.

CLINICALTRIAL

Identifying and prioritizing resilient health system units to tackle the COVID-19 pandemic

Since human health greatly depends on a healthy and risk-free social environment, it is very important to have a concept to focus on improving epidemiology capacity and potential along with economic perspectives as a very influential factor in the future of societies. Through responsible behavior during an epidemic crisis, the health system units can be utilized as a suitable platform for sustainable development. This study employs the Best-Worst Method (BWM) in order to develop a system for identifying and ranking health system units with understanding the nature of the epidemic to help the World Health Organization (WHO) in recognizing the capabilities of resilient health system units. The purpose of this study is to identify and prioritize the resilient health system units for dealing with Coronavirus. The statistical population includes 215 health system units in the world and the opinions of twenty medical experts are also utilized as an informative sample to localize the conceptual model of the study and answer the research questionnaires. The resilient health system units of the world are identified and prioritized based on the statistics of "Total Cases", "Total Recovered", "Total Deaths", "Active Cases", "Serious", "Total Tests" and "Day of Infection". The present descriptive cross-sectional study is conducted on Worldometer data of COVID-19 during the period of 17 July 2020 at 8:33 GMT. According to the results, the factors of "Total Cases", "Total Deaths", "Serious", "Active Cases", "Total Recovered", "Total Tests" and "Day of Infection" are among the most effective ones, respectively, in order to have a successful and optimal performance during a crisis. The attention of health system units to the identified important factors can improve the performance of epidemiology system. The WHO should pay more attention to low-resilience health system units in terms of promoting the health culture in crisis management of common viruses. Considering the importance of providing health services as well as their significant effect on the efficiency of the world health system, especially in critical situations, resilience analysis with the possibility of comparison and ranking can be an important step to continuously improve the performance of health system units.

Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference

BACKGROUND

One of the main challenges of the COVID-19 pandemic is to make sense of available, but often heterogeneous and noisy data. This contribution presents a data-driven methodology that allows exploring the hospitalization dynamics of COVID-19, exemplified with a study of 17 autonomous regions in Spain from summer 2020 to summer 2021.

METHODS

We use data on new daily cases and hospitalizations reported by the Spanish Ministry of Health to implement a Bayesian inference method that allows making short-term predictions of bed occupancy of COVID-19 patients in each of the autonomous regions of the country.

RESULTS

We show how to use the temporal series for the number of daily admissions and discharges from hospital to reproduce the hospitalization dynamics of COVID-19 patients. For the case-study of the region of Aragon, we estimate that the probability of being admitted to hospital care upon infection is 0.090 [0.086-0.094], (95% C.I.), with the distribution governing hospital admission yielding a median interval of 3.5 days and an IQR of 7 days. Likewise, the distribution on the length of stay produces estimates of 12 days for the median and 10 days for the IQR. A comparison between model parameters for the regions analyzed allows to detect differences and changes in policies of the health authorities.

CONCLUSIONS

We observe important regional differences, signaling that to properly compare very different populations, it is paramount to acknowledge all the diversity in terms of culture, socio-economic status, and resource availability. To better understand the impact of this pandemic, much more data, disaggregated and properly annotated, should be made available.

Digital cities and the spread of COVID-19: Characterizing the impact of non-pharmaceutical interventions in five cities in Spain

Mathematical modeling has been fundamental to achieving near real-time accurate forecasts of the spread of COVID-19. Similarly, the design of non-pharmaceutical interventions has played a key role in the application of policies to contain the spread. However, there is less work done regarding quantitative approaches to characterize the impact of each intervention, which can greatly vary depending on the culture, region, and specific circumstances of the population under consideration. In this work, we develop a high-resolution, data-driven agent-based model of the spread of COVID-19 among the population in five Spanish cities. These populations synthesize multiple data sources that summarize the main interaction environments leading to potential contacts. We simulate the spreading of COVID-19 in these cities and study the effect of several non-pharmaceutical interventions. We illustrate the potential of our approach through a case study and derive the impact of the most relevant interventions through scenarios where they are suppressed. Our framework constitutes a first tool to simulate different intervention scenarios for decision-making.

Genomic Epidemiology Unveil the Omicron Transmission Dynamics in Rome, Italy

Since 2020, the COVID-19 pandemic represented an important worldwide burden. Well-structured surveillance by reliable and timely genomic data collection is crucial. In this study, a genomic monitoring analysis of all SARS-CoV-2 positive samples retrieved at the Fondazione Policlinico Universitario Campus Bio-Medico, in Rome, Italy, between December 2021 and June 2022, was performed. Two hundred and seventy-four SARS-CoV-2-positive samples were submitted to viral genomic sequencing by Illumina MiSeqII. Consensus sequences were generated by de novo assembling using the iVar tool and deposited on the GISAID database. Lineage assignment was performed using the Pangolin lineage classification. Sequences were aligned using ViralMSA and maximum-likelihood phylogenetic analysis was performed by IQ-TREE2. TreeTime tool was used to obtain dated trees. Our genomic monitoring revealed that starting from December 2021, all Omicron sub-lineages (BA.1, BA.2, BA.3, BA.4, and BA.5) were circulating, although BA.1 was still the one with the highest prevalence thought time in this early period. Phylogeny revealed that Omicron isolates were scattered throughout the trees, suggesting multiple independent viral introductions following national and international human mobility. This data represents a sort of thermometer of what happened from July 2021 to June 2022 in Italy. Genomic monitoring of the circulating variants should be encouraged considering that SARS-CoV-2 variants or sub-variants emerged stochastically and unexpectedly.

An epidemic model for SARS-CoV-2 with self-adaptive containment measures

During the COVID-19 pandemic, several countries have resorted to self-adaptive mechanisms that tailor non-pharmaceutical interventions to local epidemiological and health care indicators. These mechanisms reinforce the mutual influence between containment measures and the evolution of the epidemic. To account for such interplay, we develop an epidemiological model that embeds an algorithm mimicking the self-adaptive policy mechanism effective in Italy between November 2020 and March 2022. This extension is key to tracking the historical evolution of health outcomes and restrictions in Italy. Focusing on the epidemic wave that started in mid-2021 after the diffusion of Delta, we compare the functioning of alternative mechanisms to show how the policy framework may affect the trade-off between health outcomes and the restrictiveness of mitigation measures. Mechanisms based on the reproduction number are generally highly responsive to early signs of a surging wave but entail severe restrictions. The emerging trade-off varies considerably depending on specific conditions (e.g., vaccination coverage), with less-reactive mechanisms (e.g., those based on occupancy rates) becoming more appealing in favorable contexts.