Volume 11, Issue s1 - Covid-19: lessons learned in the use of epidemiologic methods

Objectives Recent studies show that Test Positivity Rate (TPR) gains a better correlation than incidence with the number of hospitalized patients in COVID-19 pandemic. Nevertheless, epidemiologists remain sceptical concerning the widespread use of this metric for surveillance, and indicators based on known cases like incidence rate are still preferred despite the large number of asymptomatic carriers, which remain unknown. Our aim is to compare TPR and incidence rate, to determine which of the two has the best characteristics to predict the trend of hospitalized patients in the COVID-19 pandemic. Methods We perform a retrospective study considering 60 outbreak cases, using global and local data from Italy in different waves of the pandemic, in order to detect peaks in TPR time series, and peaks in incidence rate, finding which of the two indicators has the best ability to anticipate peaks in patients admitted in hospitals. Results On average, the best TPR-based approach anticipates the incidence rate of about 4.6 days (95 % CI 2.8, 6.4), more precisely the average distance between TPR peaks and hospitalized peaks is 17.6 days (95 % CI 15.0, 20.4) with respect to 13.0 days (95 % CI 10.4, 15.8) obtained for incidence. Moreover, the average difference between TPR and incidence rate increased to more than 6 days in the Delta outbreak during summer 2021, where presumably the percentage of asymptomatic carriers was larger. Conclusions We conclude that TPR should be used as the primary indicator to enable early intervention, and for predicting hospital admissions in infectious diseases with asymptomatic carriers.

Objectives To determine the point prevalence of undergraduate students who are hesitant to accept COVID-19 vaccination and to identify the predictors of COVID-19 vaccine hesitancy in university students. Methods A cross-sectional study was conducted during June–July 2021. A total of 542 undergraduate students from universities in three central provinces of Thailand participated in an online survey via Google Form. We used a transculturally translated, Thai version of the Oxford Coronavirus Explanations, Attitudes, and Narratives Survey (OCEANS II). Results There were 217 undergraduate students (40%) who were hesitant to receive the COVID-19 vaccine and the significant predictors for this hesitancy were: being students in Year 2 and higher (AOR: 2.73; 95% CI: 1.55–4.84); having negative beliefs toward the COVID-19 vaccine (AOR: 10.99; 95% CI: 6.82–17.73); and having a perceived positive general vaccine conspiracy belief (AOR: 1.90; 95% CI: 1.02–3.52). Conclusions It is important to minimize vaccine hesitancy among Thai undergraduate students with a negative perception of vaccines by clarifying false information.

Objectives To forecast the true growth of COVID-19 cases in Singapore after accounting for asymptomatic infections, we study and make modifications to the SEIR (Susceptible-Exposed-Infected-Recovered) epidemiological model by incorporating hospitalization dynamics and the presence of asymptomatic cases. We then compare the simulation results of our three epidemiological models of interest against the daily reported COVID-19 case counts during the time period from 23rd January to 6th April 2020. Finally, we compare and evaluate on the performance and accuracy of the aforementioned models’ simulations. Methods Three epidemiological models are used to forecast the true growth of COVID-19 case counts by accounting for asymptomatic infections in Singapore. They are the exponential model, SEIR model with hospitalization dynamics (SEIHRD), and the SEIHRD model with inclusion of asymptomatic cases (SEAIHRD). Results Simulation results of all three models reflect underestimation of COVID-19 cases in Singapore during the early stages of the pandemic. At a 40% asymptomatic proportion, we report basic reproduction number R 0 = 3.28 and 3.74 under the SEIHRD and SEAIHRD models respectively. At a 60% asymptomatic proportion, we report R 0 = 3.48 and 3.96 under the SEIHRD and SEAIHRD models respectively. Conclusions Based on the results of different simulation scenarios, we are highly confident that the number of COVID-19 cases in Singapore was underestimated during the early stages of the pandemic. This is supported by the exponential increase of COVID-19 cases in Singapore as the pandemic evolved.

Objectives The ability to predict COVID-19 dynamic has been very low, reflected in unexpected changes in the number of cases in different settings. Here the objective was to study the temporal memory of the reported daily incidence time series and propose a simple model for short-term forecast of the incidence. Methods We propose a new concept called incidence moments that allows exploring the memory of the reported incidence time series, based on successive products of the incidence and the reproductive number that allow a short term forecast of the future incidence. We studied the correlation between the predictions of and the reported incidence determining the best predictor. We compared the predictions and observed COVID-19 incidences with the mean arctangent absolute percentage error (MAAPE) analyses for the world, 43 countries and for Chile and its regions. Results The best predictor was the third moment of incidence, determining a short temporal prediction window of 15 days. After 15 days the absolute percentage error of the prediction increases significantly. The method perform better for larger populations and presents distortions in contexts of abrupt changes in incidence. Conclusions The epidemic dynamics of COVID 19 had a very short prediction window, probably associated with an intrinsic chaotic behavior of its dynamics. The incident moment modeling approach could be useful as a tool whose simplicity is appealing, since it allows rapid implementation in different settings, even with limited epidemiological technical capabilities and without requiring a large amount of computational data.

Objectives The main objective of this research is to demonstrate the effectiveness of non-pharmaceutical interventions (social isolation and quarantine) and of vaccination. Methods The SIR epidemiological numerical model has been revised to obtain a new model (SAIRDQ), which involves additional variables: the population that died due to the disease ( D ), the isolated ( A ), quarantined population ( Q ) and the effect of vaccination. We have obtained the epidemiological parameters from the data, which are not constant during the evolution of the pandemic, using an iterative approximation method. Results Analysis of the data of infected and deceased suggest that the evolution of the coronavirus epidemic in Peru has arrived at the end of the second wave (around October 2021). We have simulated the effect of quarantine and vaccination, which are effective measures to reduce the impact of the pandemic. For a variable infection and isolation rate, due to the end of the quarantine, the death toll would be around 200 thousand; if the isolation and quarantine were relaxed since March 01, 2021, there could be more than 280 thousand deaths. Conclusions Without non-pharmaceutical interventions and vaccination, the number of deaths would be much higher than 280 thousand.