SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework.

BACKGROUND: Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS: This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS: We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend.

BACKGROUND: Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. METHODS: We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. RESULTS: Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. CONCLUSION: Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

Symptoms of post COVID-19 condition and diseases/conditions diagnosed after COVID-19 in Japanese patients: A real-world study using a claims database.

BACKGROUND: More than 200 symptoms of post coronavirus disease (COVID-19) condition (PCC) impacting patients' quality of life have been reported. This study describes the symptoms of well-known PCC and diseases/conditions diagnosed after COVID-19 and analyzes the trends in well-known PCC according to the epidemic waves in the Japanese population. METHODS: Patients with a COVID-19 diagnosis in the JMDC claims database were matched 1:1 with individuals without COVID-19 diagnosis (controls) based on sex, year and month of birth, and risk factors for aggravation. The first month of COVID-19 diagnosis from January 2020-March 2022 was the index month, and the observation period was from July 2019 to 6 months from the index month (patients) and July 2019-September 2022 (controls). RESULTS: Of 263,456 each of patients and controls after matching, 51.8 % were aged 18-49 years, 56.3 % were male, and 24.5 % had risk factors for aggravation. One in 18 patients experienced well-known PCC 2-3 months after severe acute respiratory syndrome cornonavirus 2 infection, with the highest odds ratio (OR) being for pulmonary thromboembolism (29.37), followed by smell/taste disorder (13.34) and respiratory failure (8.28). Some of the common diseases/conditions diagnosed after COVID-19 comprised those of the genitourinary system, eye and adnexa, and ear and mastoid process and certain infectious and parasitic diseases. Overall, the risk difference decreased from the first to the sixth wave, but the OR was >1.00 for most symptoms even during the sixth wave. CONCLUSIONS: PCC symptoms showed a declining trend over time but persisted. Physicians and patients need to recognize PCC symptoms.

A KdV-SIR equation and its analytical solutions: An application for COVID-19 data analysis.

To describe the time evolution of infected persons associated with an epidemic wave, we recently derived the KdV-SIR equation that is mathematically identical to the Kortewegde Vries (KdV) equation in the traveling wave coordinate and that represents the classical SIR model under a weakly nonlinear assumption. This study further discusses the feasibility of applying the KdV-SIR equation and its analytical solutions together with COVID-19 data in order to estimate a peak time for a maximum number of infected persons. To propose a prediction method and to verify its performance, three types of data were generated based on COVID-19 raw data, using the following procedures: (1) a curve fitting package, (2) the empirical mode decomposition (EMD) method, and (3) the 28-day running mean method. Using the produced data and our derived formulas for ensemble forecasts, we determined various estimates for growth rates, providing outcomes for possible peak times. Compared to other methods, our method mainly relies on one parameter, σo (i.e., a time independent growth rate), which represents the collective impact of a transmission rate (ß) and a recovery rate (ν). Utilizing an energy equation that describes the relationship between the time dependent and independent growth rates, our method offers a straightforward alternative for estimating peak times in ensemble predictions.

Multiple waves of COVID-19: a pathway model approach.

A generalized pathway model, with time-dependent parameters, is applied to describe the mortality curves of the COVID-19 disease for several countries that exhibit multiple waves of infections. The pathway approach adopted here is formulated explicitly in time, in the sense that the model's growth rate for the number of deaths or infections is written as an explicit function of time, rather than in terms of the cumulative quantity itself. This allows for a direct fit of the model to daily data (new deaths or new cases) without the need of any integration. The model is applied to COVID-19 mortality curves for ten selected countries and found to be in very good agreement with the data for all cases considered. From the fitted theoretical curves for a given location, relevant epidemiological information can be extracted, such as the starting and peak dates for each successive wave. It is argued that obtaining reliable estimates for such characteristic points is important for studying the effectiveness of interventions and the possible negative impact of their relaxation, as it allows for a direct comparison of the time of adoption/relaxation of control measures with the peaks and troughs of the epidemic curve.

Spatiotemporal analysis of highly pathogenic avian influenza (H5N1) outbreaks in poultry in Egypt (2006 to 2017).

BACKGROUND: In Egypt, the highly pathogenic avian influenza (HPAI) subtype H5N1 is endemic and possesses a severe impact on the poultry. To provide a better understanding of the distributional characteristics of HPAI H5N1 outbreaks in Egypt, this study aimed to explore the spatiotemporal pattern and identify clusters of HPAI H5N1 outbreaks in Egypt from 2006 to 2017. RESULTS: The Epidemic curve (EC) was constructed through time series analysis; in which six epidemic waves (EWs) were revealed. Outbreaks mainly started in winter peaked in March and ended in summer. However, newly emerged thermostable clades (2.2.1.1 and 2.2.1.2) during the 4th EW enabled the virus to survive and cause infection in warmer months with a clear alteration in the seasonality of the epidemic cycle in the 5th EW. The endemic situation became more complicated by the emergence of new serotypes. As a result, the EC ended up without any specific pattern since the 6th EW to now. The spatial analysis showed that the highest outbreak density was recorded in the Nile Delta considering it as the 'Hot spot' region. By the 6th EW, the outbreak extended to include the Nile valley. From spatiotemporal cluster epidemics, clustering in the Delta was a common feature in all EWs with primary clusters consistently detected in the hot-spot region, but the location and size varied with each EW. The highest Relative Risk (RR) regions in an EW were noticed to contain the primary clusters of the next EW and were found to include stopover sites for migratory wild birds. They were in Fayoum, Dakahlia, Qalyobiya, Sharkia, Kafr_Elsheikh, Giza, Behera, Menia, and BeniSuef governorates. Transmission of HPAI H5N1 occurred from one location to another directly resulted in a series of outbreaks forming neighboring secondary clusters. The absence of geographical borders between the governorates in addition to non-restricted movements of poultry and low vaccination and surveillance coverage contributed to the wider spread of infection all over Egypt and to look like one epidemiological unit. CONCLUSION: Our findings can help in better understanding of the characteristics of HPAI H5N1 outbreaks and the distribution of outbreak risk, which can be used for effective disease control strategies.

Comparing the First and Second Wave of COVID-19 in Kidney Transplant Recipients: An East-European Perspective.

BACKGROUND: The present study examined whether patient characteristics, management, and outcome of kidney transplant recipients (KTx) with COVID-19 changed in the second versus the first pandemic wave. METHODS: We reviewed all available data (demographics, medical history, comorbidities, therapeutic interventions, and outcome) on our KTx with COVID-19 during the first wave (March-September 2020, n = 33) and the second wave (October 2020-February 2021, n = 149) of the COVID-19 pandemic. RESULTS: One hundred eighty-two out of our 1,503 KTx in active follow-up got COVID-19 during 12-month period, corresponding to a prevalence of 12.1%. No difference was found in age, gender distribution, comorbidities, body mass index, or baseline immunosuppression between the 2 COVID-19 waves. Bilateral COVID pneumonia was more frequent during the first wave. More KTx were managed as outpatients during the second wave (15 vs. 39%, p < 0.01). Calcineurin inhibitors were more sparingly reduced during the second wave, whereas antimetabolites were similarly reduced (91 vs. 86, p = ns). Admission to intensive care units was comparable between the first (27%) and second waves (23%). During the first wave, 8 out of 9 patients (89%) requiring intensive care died, whereas the mortality of the ICU patients in the second wave was 68% (23 deaths) (p = 0.2). The overall mortality was 24% during the first wave and 16% during the second wave (p = 0.21), while in-hospital mortality was identical between the CO-VID-19 waves (27%). Increasing age and poor allograft function were significant predictors of mortality. CONCLUSIONS: Most patient characteristics and outcome were comparable between the first 2 COVID-19 waves. More KTx were managed as outpatients without an overall negative impact on outcome.

[Haute-Marne (France) experience during the 5th COVID-19 epidemic wave in a geriatric short-stay unit]. / Expérience haute-marnaise (France) au cours de la 5ème vague épidémique COVID-19 au sein d'une unité de court séjour gériatrique.

OBJECTIVE: The objective is to study the contribution of vaccination against COVID-19 in elderly subjects infected with COVID-19. METHOD: a retrospective study with screening of medical records was carried out among patients affected by an infection linked to COVID-19 during the 5th epidemic wave that required admission in a French hospital. RESULTS: 51 subjects were included, with 25 women (49 %), mean age 85 y+/-6 years. Among the 51 patients, 28 patients were hospitalized specifically for the management of COVID-19 infection and 25 (49 %) patients showed signs of severity on admission; 35 patients (68.6 %) were vaccinated, with a complete vaccination schedule (2 doses). We noted 16 deaths (31.4 %). Oxygen saturation on admission was significantly associated with the rates of COVID lesions on chest CT (p = 0.018), yet the correlation remained moderate. Vaccination had a significant protective effect on patient death (p = 0.0236). CONCLUSION: Vaccination is more than ever the only weapon of prevention against serious complications related to COVID-19 infection, especially in very elderly subjects, exposed to several comorbidities and polymedication. Prevention efforts and dedicated health explanations are the keys to a useful vaccination being targeted on elderly populations at risk.

: Objectif : L'objectif du travail est d'étudier l'apport de la vaccination contre la COVID-19 chez les sujets âgés infectés par la COVID-19. Méthode : une étude rétrospective sur consultation de dossiers médicaux a été réalisée avec inclusion de patients atteints par une infection liée à la COVID-19 au cours de la 5e vague épidémique en France. Résultats : 51 sujets ont été inclus dont 25 femmes (49 %), avec un âge moyen de 85 ± 6 années. Parmi ceux-ci, 28 patients étaient hospitalisés spécifiquement pour la prise en charge de cette infection; 25 (49 %) patients présentaient des signes de gravité à leur admission; 35 patients (68,6 %) étaient vaccinés (schéma vaccinal complet avec 2 doses). Nous avons noté 16 décès (31,4 %). On note que la saturation en oxygène à l'admission est significativement associée aux taux de lésions COVID au scanner thoracique (p = 0,018), même si la corrélation reste modérée. La vaccination a un effet de protection significatif concernant les décès (p = 0,0236). Conclusion : La vaccination est plus que jamais la seule arme de prévention contre les complications graves liées à l'infection COVID-19, notamment chez les sujets très âgés, présentant diverses comorbidités et polymédiqués. Les efforts de prévention et les explications sanitaires sont les clés pour une vaccination utile et ciblée sur des populations à risque telles que les sujets âgés.

Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties.

This paper uses data from the United States to examine determinants of the spread of COVID-19 during three different epidemic waves. We address how sociodemographic and economic attributes, industry composition, density, crowding in housing, and COVID-19-related variables are associated with the transmission of COVID-19. After controlling for spatial autocorrelation, our findings indicate that the percentage of people in poverty, number of restaurants, and percentage of workers teleworking were associated with the COVID-19 incidence rate during all three waves. Our results also show that dense areas were more vulnerable to the transmission of COVID-19 after the first epidemic wave. Regarding the density of supermarkets, our study elaborates the negative aspects of wholesale retail stores, which likely provide a vulnerable place for virus transmission. Our results suggest that sociodemographic and economic attributes were the determinants of the early phase of the pandemic, while density showed positive association with the transmission during subsequent waves. We provide implications for regions serving as gateway cities with high density and number of population. To add, we further provide evidence that non-pharmaceutical interventions in the early stage may mitigate the virus transmission.

Growth and decline of the COVID-19 epidemic wave in Italy from March to June 2020.

March 21, 2020 was the ridgeline between the growth of the coronavirus disease 2019 (COVID-19) epidemic wave in Italy and the start of its decline. We analyzed the epidemic patterns from March 1 to June 30. There was a progressive drop of cases from March (104,710) to April (94,888), May (25,705) and June (8110). Likewise, after a slight increase of deaths in April (14,804) compared to March (12,396), a considerable decline occurred in May (5170) and June (1464). Doubling times of cumulative cases grew from 2 to 6 days until March 20 to 2 weeks up to April 5, and thereafter no further doubling occurred until June 30. There was a striking North-South gradient of both cases and deaths. At the end of June, the nine Northern Italian regions or provinces, five central regions, and seven southern regions had contributed to the 81.1%, 12.4%, and 6.5% of the 240,578 national cases, respectively. Lombardy, the most populous region, was by far the most heavily affected one, accounting for the 39.0% of the national cases occurring over the analyzed 4-month period. However, in relative terms, it was preceded by Aosta Valley, the least populous region, less than 1% of the population of both regions having been affected by cases of COVID-19. The curves showing the ratio of daily cumulative cases and deaths to those of the previous day tended to flatten with time by approaching the zero growth but without reaching it, which documents a persisting circulation of severe acute respiratory syndrome coronavirus 2 in the Italian territory.

Second versus first wave of COVID-19 deaths: Shifts in age distribution and in nursing home fatalities.

OBJECTIVE: To examine whether the age distribution of COVID-19 deaths and the share of deaths in nursing homes changed in the second versus the first pandemic wave. ELIGIBLE DATA: We considered all countries that had at least 4000 COVID-19 deaths occurring as of January 14, 2021, at least 200 COVID-19 deaths occurring in each of the two epidemic wave periods; and which had sufficiently detailed information available on the age distribution of these deaths. We also considered countries with data available on COVID-19 deaths of nursing home residents for the two waves. MAIN OUTCOME MEASURES: Change in the second wave versus the first wave in the proportion of COVID-19 deaths occurring in people <50 years ("young deaths") among all COVID-19 deaths and among COVID-19 deaths in people <70 years old; and change in the proportion of COVID-19 deaths in nursing home residents among all COVID-19 deaths. RESULTS: Data on age distribution were available for 14 eligible countries. Individuals <50 years old had small absolute difference in their share of the total COVID-19 deaths in the two waves across 13 high-income countries (absolute differences 0.0-0.4%). Their proportion was higher in Ukraine, but it decreased markedly in the second wave. The proportion of young deaths was lower in the second versus the first wave (summary prevalence ratio 0.81, 95% CI 0.71-0.92) with large between-country heterogeneity. The proportion of young deaths among deaths <70 years did not differ significantly across the two waves (summary prevalence ratio 0.96, 95% CI 0.86-1.06). Eligible data on nursing home COVID-19 deaths were available for 11 countries. The share of COVID-19 deaths that were accounted by nursing home residents decreased in the second wave significantly and substantially in 8 countries (prevalence ratio estimates: 0.36 to 0.78), remained the same in Denmark and Norway and markedly increased in Australia. CONCLUSIONS: In the examined countries, age distribution of COVID-19 deaths has been fairly similar in the second versus the first wave, but the contribution of COVID-19 deaths in nursing home residents to total fatalities has decreased in most countries in the second wave.

A novel sub-epidemic modeling framework for short-term forecasting epidemic waves.

BACKGROUND: Simple phenomenological growth models can be useful for estimating transmission parameters and forecasting epidemic trajectories. However, most existing phenomenological growth models only support single-peak outbreak dynamics whereas real epidemics often display more complex transmission trajectories. METHODS: We develop and apply a novel sub-epidemic modeling framework that supports a diversity of epidemic trajectories including stable incidence patterns with sustained or damped oscillations to better understand and forecast epidemic outbreaks. We describe how to forecast an epidemic based on the premise that the observed coarse-scale incidence can be decomposed into overlapping sub-epidemics at finer scales. We evaluate our modeling framework using three outbreak datasets: Severe Acute Respiratory Syndrome (SARS) in Singapore, plague in Madagascar, and the ongoing Ebola outbreak in the Democratic Republic of Congo (DRC) and four performance metrics. RESULTS: The sub-epidemic wave model outperforms simpler growth models in short-term forecasts based on performance metrics that account for the uncertainty of the predictions namely the mean interval score (MIS) and the coverage of the 95% prediction interval. For example, we demonstrate how the sub-epidemic wave model successfully captures the 2-peak pattern of the SARS outbreak in Singapore. Moreover, in short-term sequential forecasts, the sub-epidemic model was able to forecast the second surge in case incidence for this outbreak, which was not possible using the simple growth models. Furthermore, our findings support the view that the national incidence curve of the Ebola epidemic in DRC follows a stable incidence pattern with periodic behavior that can be decomposed into overlapping sub-epidemics. CONCLUSIONS: Our findings highlight how overlapping sub-epidemics can capture complex epidemic dynamics, including oscillatory behavior in the trajectory of the epidemic wave. This observation has significant implications for interpreting apparent noise in incidence data where the oscillations could be dismissed as a result of overdispersion, rather than an intrinsic part of the epidemic dynamics. Unless the oscillations are appropriately modeled, they could also give a false positive, or negative, impression of the impact from public health interventions. These preliminary results using sub-epidemic models can help guide future efforts to better understand the heterogenous spatial and social factors shaping sub-epidemic patterns for other infectious diseases.

Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19.

BACKGROUND: Predicting the endemic/epidemic transition during the temporal evolution of a contagious disease. METHODS: Indicators for detecting the transition endemic/epidemic, with four scalars to be compared, are calculated from the daily reported news cases: coefficient of variation, skewness, kurtosis, and entropy. The indicators selected are related to the shape of the empirical distribution of the new cases observed over 14 days. This duration has been chosen to smooth out the effect of weekends when fewer new cases are registered. For finding a forecasting variable, we have used the principal component analysis (PCA), whose first principal component (a linear combination of the selected indicators) explains a large part of the observed variance and can then be used as a predictor of the phenomenon studied (here the occurrence of an epidemic wave). RESULTS: A score has been built from the four proposed indicators using the PCA, which allows an acceptable level of forecasting performance by giving a realistic retro-predicted date for the rupture of the stationary endemic model corresponding to the entrance in the epidemic exponential growth phase. This score is applied to the retro-prediction of the limits of the different phases of the COVID-19 outbreak in successive endemic/epidemic transitions for three countries, France, India, and Japan. CONCLUSION: We provided a new forecasting method for predicting an epidemic wave occurring after an endemic phase for a contagious disease.

Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022-2023 mpox epidemic.

During the 2022-2023 unprecedented mpox epidemic, near real-time short-term forecasts of the epidemic's trajectory were essential in intervention implementation and guiding policy. However, as case levels have significantly decreased, evaluating model performance is vital to advancing the field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention and Our World in Data teams, we generated retrospective sequential weekly forecasts for Brazil, Canada, France, Germany, Spain, the United Kingdom, the United States and at the global scale using an auto-regressive integrated moving average (ARIMA) model, generalized additive model, simple linear regression, Facebook's Prophet model, as well as the sub-epidemic wave and n-sub-epidemic modelling frameworks. We assessed forecast performance using average mean squared error, mean absolute error, weighted interval scores, 95% prediction interval coverage, skill scores and Winkler scores. Overall, the n-sub-epidemic modelling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best most frequently. The n-sub-epidemic and spatial-wave frameworks considerably improved in average forecasting performance relative to the ARIMA model (greater than 10%) for all performance metrics. Findings further support sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.

Data-driven mathematical modeling approaches for COVID-19: A survey.

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).

[Effect of the Omicron variant on cumulative incidence of infection and lethality during the sixth wave of the COVID-19 epidemic in Spain]. / Efecto de la variante Ómicron sobre la incidencia y la letalidad durante la 6.ª onda epidémica COVID-19 en España.

The COVID-19 pandemic has strained healthcare systems globally. The successive epidemic waves have shown different characteristics. The Omicron variant of SARS-CoV-2 modified the epidemic behavior that previous variants had followed. The aim of this analysis was to determine the epidemiological characteristics of COVID-19 during the sixth epidemic wave and its differences according to the predominance of the Delta or Omicron variants. The epidemiological data corresponding to the sixth wave of the epidemic published by official organizations were analyzed, and the cumulative incidence of infection (CI-I) and case fatality rates (CFR) were calculated, both for Spain as a whole and for the different Autonomous Communities, in the population as a whole and by age groups. The results showed that the CI-I was higher with the Ómicron variant (10.89% vs 0.75% with Delta) while the CFR was higher with the Delta variant (4.2‰ vs 1.3‰ with Ómicron), as well as a higher rate of hospitalization and ICU admission with the Delta variant.

Associated factors for mortality in a COVID-19 colombian cohort: is the third wave relevant when Mu variant was predominant epidemiologically?

OBJECTIVES: To evaluate the association between Colombia's third wave when the Mu variant was predominant epidemiologically (until 75%) in Colombia and COVID-19 all-cause in-hospital mortality. METHODS: In this retrospective cohort, we included hospitalized patients ≥18 years with SARS-CoV-2 infection between March 2020 to September 2021 in ten hospitals from three cities in Colombia. Description analysis, survival, and multivariate Cox regression analyses were performed to evaluate the association between the third epidemic wave and in-hospital mortality. RESULTS: A total of 25,371 patients were included. The age-stratified time-to-mortality curves showed differences according to epidemic waves in patients ≥75 years (log-rank test p = 0.012). In the multivariate Cox analysis, the third wave was not associated with increased mortality relative to the first wave (aHR 0.95; 95%CI 0.84-1.08), but there was an interaction between age ≥75 years and the third wave finding a lower HR for mortality (aHR 0.56, 95%CI 0.36-0.86). CONCLUSIONS: We did not find an increase in in-hospital mortality during the third epidemic wave in which the Mu variant was predominant in Colombia. The reduced hazard in mortality in patients ≥75 years hospitalized in the third wave could be explained by the high coverage of SARS-CoV-2 vaccination in this population and patients with underlying conditions.

Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic.

Introduction: A discussion of 'waves' of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous. Methods: We present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as 'observed waves'. This provides an objective means of describing observed waves in time series. We use this method to synthesize evidence across different countries to study types, drivers and modulators of waves. Results: The output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of NPIs correlates with a reduced number of observed waves and reduced mortality burden in those waves. Conclusion: It is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.

Statistical Analysis of Mortality Rates of Coronavirus Disease 2019 (COVID-19) Patients in Japan Across the 4C Mortality Score Risk Groups, Age Groups, and Epidemiological Waves: A Report From the Nationwide COVID-19 Cohort.

Background: The mortality rates of coronavirus disease 2019 (COVID-19) have been changed across the epidemiological waves. The aim was to investigate the differences in mortality rates of COVID-19 patients in Japan across the 6 epidemiological waves stratified by age group and Coronavirus Clinical Characterisation Consortium (4C) mortality score risk group. Methods: A total of 56 986 COVID-19 patients in the COVID-19 Registry Japan from 2 March 2020 to 1 February 2022 were enrolled. These patients were categorized into 4 risk groups based on their 4C mortality score. Mortality rates of each risk group were calculated separately for different age groups: 18-64, 65-74, 75-89, and ≥90 years. In addition, mortality rates across the wave periods were calculated separately in 2 age groups: <75 and ≥75 years. All calculated mortality rates were compared with reported data from the United Kingdom (UK) during the early epidemic. Results: The mortality rates of patients in Japan were significantly lower than in the UK across the board, with the exception of patients aged ≥90 years at very high risk. The mortality rates of patients aged ≥75 years at very high risk in the fourth and fifth wave periods showed no significant differences from those in the UK, whereas those in the sixth wave period were significantly lower in all age groups and in all risk groups. Conclusions: The present analysis showed that COVID-19 patients had a lower mortality rate in the most recent sixth wave period, even among patients ≥75 years old at very high risk.

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.

Introduction: Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. Methods: The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Results: Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Conclusion: Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.