Test positivity - Evaluation of a new metric to assess epidemic dispersal mediated by non-symptomatic cases

A Low-Cost Early Warning Method for Infectious Diseases with Asymptomatic Carriers

At the beginning of 2023, the Italian former prime minister, the former health minister and 17 others including the current president of the Lombardy region were placed under investigation on suspicion of aggravated culpable epidemic in connection with the government's response at the start of the COVID-19 pandemic. The charges revolve around the failure by authorities to take adequate measures to prevent the spread of the virus in the Bergamo area, which experienced a significant excess of deaths during the initial outbreak. The aim of this paper is to analyse the pandemic data of Italy and the Lombardy region in the first 10 days of the pandemic, spanning from the 24th of February 2020 to the 4th of March 2020. The objective is to determine whether the use of early warning indicators could have facilitated the identification of a critical increase in infections. This identification, in turn, would have enabled the timely formulation of strategies for pandemic containment, thereby reducing the number of deaths. In conclusion, to translate our findings into practical guidelines, we propose a low-cost early warning method for infectious respiratory diseases with asymptomatic carriers.

Heterogeneity in COVID-19 infection among older persons in South Africa: Evidence from national surveillance data

Background

The 2021 World Health Organization study on the impact of COVID-19 on older people (≥60 years) in the African region highlighted the difficulties they faced as the virus spread across borders and dominated the way of life. These difficulties included disruptions to both essential health care services and social support, as well as disconnections from family and friends. Among those who contracted COVID-19, the risks of severe illness, complications, and mortality were highest among near-old and older persons.

Objective

Recognizing that older persons are a diverse group including younger- and older-aged individuals, a study was conducted to track the epidemic among near-old (50–59 years) and older persons (≥60 years) in South Africa covering the 2 years since the epidemic emerged.

Methods

Using a quantitative secondary research approach, data for near-old and older persons were extracted for comparative purposes. COVID-19 surveillance outcomes (confirmed cases, hospitalizations, and deaths) and vaccination data were compiled up to March 5th, 2022. COVID-19 surveillance outcomes were plotted by epidemiological week and epidemic waves to visualize the overall growth and trajectory of the epidemic. Means for each age-group and by COVID-19 waves, together with age-specific rates, were calculated.

Results

Average numbers of new COVID-19 confirmed cases and hospitalizations were highest among people aged 50–59- and 60–69-years. However, average age-specific infection rates showed that people aged 50–59 years and ≥80 years were most vulnerable to contracting COVID-19. Age-specific hospitalization and death rates increased, with people aged ≥ 70 years most affected. The number of people vaccinated was slightly higher among people aged 50–59 years before Wave Three and during Wave Four, but higher among people aged ≥ 60 years during Wave Three. The findings suggest that uptake of vaccinations stagnated prior to and during Wave Four for both age groups.

Discussion

Health promotion messages and COVID-19 epidemiological surveillance and monitoring are still needed, particularly for older persons living in congregate residential and care facilities. Prompt health-seeking should be encouraged, including testing and diagnosis as well as taking up vaccines and boosters, particularly for high-risk older persons.

Modelling the persistence of Covid-19 positivity rate in Italy

The current Covid-19 pandemic is severely affecting public health and global economies. In this context, accurately predicting its evolution is essential for planning and providing resources effectively. This paper aims at capturing the dynamics of the positivity rate (PPR) of the novel coronavirus using the Heterogeneous Autoregressive (HAR) model. The use of this model is motivated by two main empirical features arising from the analysis of PPR time series: the changing long-run level and the persistent autocorrelation structure. Compared to the most frequently used Autoregressive Integrated Moving Average (ARIMA) models, the HAR is able to reproduce the strong persistence of the data by using components aggregated at different interval sizes, remaining parsimonious and easy to estimate. The relative merits of the proposed approach are assessed by performing a forecasting study on the Italian dataset. As a robustness check, the analysis of the positivity rate is also conducted by considering the case of the United States. The ability of the HAR-type models to predict the PPR at different horizons is evaluated through several loss functions, comparing the results with those generated by ARIMA models. The Model Confidence Set is used to test the significance of differences in the predictive performances of the models under analysis. Our findings suggest that HAR-type models significantly outperform ARIMA specifications in terms of forecasting accuracy. We also find that the PPR could represent an important metric for monitoring the evolution of hospitalizations, as the peak of patients in intensive care units occurs within 12-16 days after the peak in the positivity rate. This can help governments in planning socio-economic and health policies in advance.