Bayesian Modeling of COVID-19 to Classify the Infection and Death Rates in a Specific Duration: The Case of Algerian Provinces

A Bayesian approach for studying COVID-19 contagion dynamics in Algeria using a Poisson autoregressive (PAR) model

Global emphasis has been focused on tracking the trends of the COVID-19 pandemic. Numerous techniques have been developed or utilized for this purpose. In this study, we seek to present and evaluate a model that, in our opinion, has not received adequate attention, using Algeria as a case study. We developed two distinct Poisson autoregressive (PAR) models using the Monte Carlo Markov Chain (MCMC) simulation method and the Bayesian method: one based solely on short-term dependence and the other incorporating both short- and long-term dependence. The study aimed to apply these models to enhance the prediction of new infections and determine whether the disease is spreading or declining. This information can guide decisions on implementing or relaxing containment measures. Our findings suggest that Algeria's epidemiological state was relatively stable at the end of the study period, with the combined long-term and short-term dependence factors being less than 1 (α + β = 0.994 ) . This indicates that while the epidemic is in decline, the infection rates are not expected to drop significantly in the near future. Furthermore, the short-term dependence parameter α = 0.987 constitutes a significant portion (99%) of the total dependence. This high value of α suggest that the COVID-19 epidemic in Algeria is experiencing a strong decline, though the rate of new infections is expected to persist at a lower level for the foreseeable future. Given these findings, it is recommended that authorities remain vigilant and continue public health measures, including educational campaigns and awareness efforts, to promote COVID-19 vaccination and adherence to health guidelines.

Disease Spread Model in Structurally Complex Spaces: An Open Markov Chain Approach

Understanding the dynamical behavior of infectious disease propagation within enclosed spaces is crucial for effectively establishing control measures. In this article, we present a modeling approach to analyze the dynamics of individuals in enclosed spaces, where such spaces are comprised of different chambers. Our focus is on capturing the movement of individuals and their infection status using an open Markov chain framework. Unlike ordinary Markov chains, an open Markov chain accounts for individuals entering and leaving the system. We categorize individuals within the system into three different groups: susceptible, carrier, and infected. A discrete-time process is employed to model the behavior of individuals throughout the system. To quantify the risk of infection, we derive a probability function that takes into account the total number of individuals inside the system and the distribution among the different groups. Furthermore, we calculate mathematical expressions for the average number of susceptible, carrier, and infected individuals at each time step. Additionally, we determine mathematical expressions for the mean number and stationary mean populations of these groups. To validate our modeling approach, we compare the theoretical and numerical models proposed in this work.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

Evaluating the impact of testing strategies for the detection of nosocomial COVID-19 in English hospitals through data-driven modeling

Introduction

During the first wave of the COVID-19 pandemic 293,204 inpatients in England tested positive for SARS-CoV-2. It is estimated that 1% of these cases were hospital-associated using European centre for disease prevention and control (ECDC) and Public Health England (PHE) definitions. Guidelines for preventing the spread of SARS-CoV-2 in hospitals have developed over time but the effectiveness and efficiency of testing strategies for preventing nosocomial transmission has not been explored.

Methods

Using an individual-based model, parameterised using multiple datasets, we simulated the transmission of SARS-CoV-2 to patients and healthcare workers between March and August 2020 and evaluated the efficacy of different testing strategies. These strategies were: 0) Testing only symptomatic patients on admission; 1) Testing all patients on admission; 2) Testing all patients on admission and again between days 5 and 7, and 3) Testing all patients on admission, and again at days 3, and 5-7. In addition to admissions testing, patients that develop a symptomatic infection while in hospital were tested under all strategies. We evaluated the impact of testing strategy, test characteristics and hospital-related factors on the number of nosocomial patient infections.

Results

Modelling suggests that 84.6% (95% CI: 84.3, 84.7) of community-acquired and 40.8% (40.3, 41.3) of hospital-associated SARS-CoV-2 infections are detectable before a patient is discharged from hospital. Testing all patients on admission and retesting after 3 or 5 days increases the proportion of nosocomial cases detected by 9.2%. Adding discharge testing increases detection by a further 1.5% (relative increase). Increasing occupancy rates, number of beds per bay, or the proportion of admissions wrongly suspected of having COVID-19 on admission and therefore incorrectly cohorted with COVID-19 patients, increases the rate of nosocomial transmission. Over 30,000 patients in England could have been discharged while incubating a non-detected SARS-CoV-2 infection during the first wave of the COVID-19 pandemic, of which 3.3% could have been identified by discharge screening. There was no significant difference in the rates of nosocomial transmission between testing strategies or when the turnaround time of the test was increased.

Discussion

This study provides insight into the efficacy of testing strategies in a period unbiased by vaccines and variants. The findings are relevant as testing programs for SARS-CoV-2 are scaled back, and possibly if a new vaccine escaping variant emerges.

A weapon to fight against pervasive Omicron: systematic actions transiting to pre-COVID normal

The Coronavirus Disease-2019 (COVID-19) pandemic is not just a health crisis but also a social crisis. Confronted with the resurgence of variants with massive infections, the triggered activities from personal needs may promote the spread, which should be considered in risk management. Meanwhile, it is important to ensure that the policy responses on citizen life to a lower level. In the face of Omicron mutations, we need to sum up the control experience accumulated, adapting strategies in the dynamic coevolution process while balancing life resumption and pandemic control, to meet challenges of future crises. We collected 46 cases occurring between 2021 and 2022, mainly from China, but also including five relevant cases from other countries around the world. Based on case studies, we combine micro-view individual needs/behaviors with macro-view management measures linking Maslow's hierarchy of needs with the transmission chain of Omicron clusters. The proposed loophole chain could help identify both individual and management loopholes in the spread of the virus. The systematic actions that were taken have effectively combated these ubiquitous vulnerabilities at lower costs and lesser time. In the dynamic coevolution process, the Chinese government has made effective and more socially acceptable prevention policies while meeting the divergent needs of the entire society at the minimum costs. Systematic actions do help maintain the balance between individuals' satisfaction and pandemic containment. This implies that risk management policies should reasonably consider individual needs and improve the cooperation of various stakeholders with targeted flexible measures, securing both public health and life resumption.

The relationship between staying at home during the pandemic and the number of conceptions: A national panel data analysis

hether the COVID-19 pandemic has changed fertility patterns is still an open question, as social isolation for long periods can impact the number of conceptions in many ways. We combine administrative data on all recent births in Brazil with daily data on individual location to estimate the relationship between the share of individuals staying close to their homes in each week and the number of conceptions in that same week, comparing municipalities with different social isolation patterns during the first semester of 2020. We find that conceptions unequivocally decline when social isolation increases. The effect is stronger for women who are between 21 and 25 years old and more educated, as well as for richer, larger, and more urban municipalities. COVID-19 is likely to change fertility across countries depending on the behavior of the population and on the lock-down measures implemented to fight the pandemic.

Artificial intelligence and machine learning responses to COVID-19 related inquiries

Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.

Improved time-varying reproduction numbers using the generation interval for COVID-19

Estimating key epidemiological parameters, such as incubation period, serial interval (SI), generation interval (GI) and latent period, is essential to quantify the transmissibility and effects of various interventions of COVID-19. These key parameters play a critical role in quantifying the basic reproduction number. With the hard work of epidemiological investigators in South Korea, estimating these key parameters has become possible based on infector-infectee surveillance data of COVID-19 between February 2020 and April 2021. Herein, the mean incubation period was estimated to be 4.9 days (95% CI: 4.2, 5.7) and the mean generation interval was estimated to be 4.3 days (95% CI: 4.2, 4.4). The mean serial interval was estimated to be 4.3, with a standard deviation of 4.2. It is also revealed that the proportion of presymptomatic transmission was ~57%, which indicates the potential risk of transmission before the disease onset. We compared the time-varying reproduction number based on GI and SI and found that the time-varying reproduction number based on GI may result in a larger estimation of Rt, which refers to the COVID-19 transmission potential around the rapid increase of cases. This highlights the importance of considering presymptomatic transmission and generation intervals when estimating the time-varying reproduction number.

Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis

Background

On September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical model to investigate the transmission dynamics of COVID-19 in Hohhot.

Methods

In this study, we first investigated the epidemiological characteristics of COVID-19 cases in Hohhot, including the spatiotemporal distribution and sociodemographic distribution. Then, we proposed a time-varying Susceptible-Quarantined Susceptible-Exposed-Quarantined Exposed-Infected-Asymptomatic-Hospitalized-Removed (SQEIAHR) model to derive the epidemic curves. The next-generation matrix method was used to calculate the effective reproduction number (Re). Finally, we explored the effects of higher stringency measures on the development of the epidemic through scenario analysis.

Results

Of the 4,889 positive infected cases, the vast majority were asymptomatic and mild, mainly concentrated in central areas such as Xincheng District. People in the 30-59 age group primarily were affected by the current outbreak, accounting for 53.74%, but females and males were almost equally affected (1.03:1). Community screening (35.70%) and centralized isolation screening (26.28%) were the main ways to identify positive infected cases. Our model predicted the peak of the epidemic on October 6, 2022, the dynamic zero-COVID date on October 15, 2022, a number of peak cases of 629, and a cumulative number of infections of 4,963 (95% confidential interval (95%CI): 4,692 ~ 5,267), all four of which were highly consistent with the actual situation in Hohhot. Early in the outbreak, the basic reproduction number (R0) was approximately 7.01 (95%CI: 6.93 ~ 7.09), and then Re declined sharply to below 1.0 on October 6, 2022. Scenario analysis of higher stringency measures showed the importance of decreasing the transmission rate and increasing the quarantine rate to shorten the time to peak, dynamic zero-COVID and an Re below 1.0, as well as to reduce the number of peak cases and final affected population.

Conclusion

Our model was effective in predicting the epidemic trends of COVID-19, and the implementation of a more stringent combination of measures was indispensable in containing the spread of the virus.

Risk factors associated with indoor transmission during home quarantine of COVID-19 patients

Purpose

The study aimed to identify potential risk factors for family transmission and to provide precautionary guidelines for the general public during novel Coronavirus disease 2019 (COVID-19) waves.

Methods

A retrospective cohort study with numerous COVID-19 patients recruited was conducted in Shanghai. Epidemiological data including transmission details, demographics, vaccination status, symptoms, comorbidities, antigen test, living environment, residential ventilation, disinfection and medical treatment of each participant were collected and risk factors for family transmission were determined.

Results

A total of 2,334 COVID-19 patients participated. Compared with non-cohabitation infected patients, cohabitated ones were younger (p = 0.019), more commonly unvaccinated (p = 0.048) or exposed to infections (p < 0.001), and had higher rates of symptoms (p = 0.003) or shared living room (p < 0.001). Risk factors analysis showed that the 2019-nCov antigen positive (OR = 1.86, 95%CI 1.40-2.48, p < 0.001), symptoms development (OR = 1.86, 95%CI 1.34-2.58, p < 0.001), direct contact exposure (OR = 1.47, 95%CI 1.09-1.96, p = 0.010) were independent risk factors for the cohabitant transmission of COVID-19, and a separate room with a separate toilet could reduce the risk of family transmission (OR = 0.62, 95%CI 0.41-0.92, p = 0.018).

Conclusion

Patients showing negative 2019-nCov antigen tests, being asymptomatic, living in a separate room with a separate toilet, or actively avoiding direct contact with cohabitants were at low risk of family transmission, and the study recommended that avoiding direct contact and residential disinfection could reduce the risk of all cohabitants within the same house being infected with COVID-19.

The Role of Vaccination and Face Mask Wearing on COVID-19 Infection and Hospitalization: A Cross-Sectional Study of the MENA Region

Since the emergence of the Coronavirus disease (COVID-19) pandemic, the disease has affected more than 675 million people worldwide, including more than 6.87 million deaths. To mitigate the effects of this pandemic, many countries established control measures to contain its spread. Their riposte was based on a combination of pharmaceutical (vaccination) and non-pharmaceutical (such as facemask wearing, social distancing, and quarantine) measures. In this way, cross-sectional research was conducted in Algeria from 23 December 2021 to 12 March 2022 to investigate the effectiveness of preventative interventions in lowering COVID-19 infection and severity. More specifically, we investigated the link between mask-wearing and infection on one side, and the relationship between vaccination and the risk of hospitalization on the other. For this purpose, we used binary logistic regression modeling that allows learning the role of mask-wearing and vaccination in a heterogeneous society with respect to compliance with barrier measures. This study determined that wearing a mask is equally important for people of all ages. Further, findings revealed that the risk of infection was 0.79 times lower among those who were using masks (odds ratio (OR) = 0.79; confidence interval (CI) 95% = 0.668-0.936; p-value = 0.006). At the same time, vaccination is a necessary preventive measure as the risk of hospitalization increases with age. Compared with those who did not get vaccinated, those who got vaccinated were 0.429 times less likely to end up in the hospital (OR = 0.429; CI95% = 0.273-0.676; p < 0.0001). The model performance demonstrates significant relationships between the dependent and independent variables, with the absence of over-dispersion in both studied models, such as the Akaike Information Criterion (AIC) scores. These findings emphasize the significance of preventative measures and immunization in the battle against the COVID-19 pandemic.

COVID-19-Associated Paediatric Inflammatory Multisystem Syndrome (PIMS-TS) in Intensive Care: A Retrospective Cohort Trial (PIMS-TS INT)

Paediatric inflammatory multisystem syndrome temporally associated with COVID-19 (PIMS-TS) is a new disease in children and adolescents that occurs after often asymptomatic or mild COVID-19. It can be manifested by different clinical symptomatology and varying severity of disease based on multisystemic inflammation. The aim of this retrospective cohort trial was to describe the initial clinical presentation, diagnostics, therapy and clinical outcome of paediatric patients with a diagnosis of PIMS-TS admitted to one of the 3 PICUs. All paediatric patients who were admitted to the hospital with a diagnosis of paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS) during the study period were enrolled in the study. A total of 180 patients were analysed. The most common symptoms upon admission were fever (81.6%, n = 147), rash (70.6%, n = 127), conjunctivitis (68.9%, n = 124) and abdominal pain (51.1%, n = 92). Acute respiratory failure occurred in 21.1% of patients (n = 38). Vasopressor support was used in 20.6% (n = 37) of cases. Overall, 96.7% of patients (n = 174) initially tested positive for SARS-CoV-2 IgG antibodies. Almost all patients received antibiotics during in-hospital stays. No patient died during the hospital stay or after 28 days of follow-up. Initial clinical presentation and organ system involvement of PIMS-TS including laboratory manifestations and treatment were identified in this trial. Early identification of PIMS-TS manifestation is essential for early treatment and proper management of patients.

Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity

Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches.

The Effect of Preventive Measures and Vaccination against SARS-CoV-2 on the Infection Risk, Treatment, and Hospitalization: A Cross-Sectional Study of Algeria

Coronavirus disease (COVID-19) caused by the SARS-CoV-2 virus continues to afflict many countries around the world. The resurgence of COVID-19 cases and deaths in many countries shows a complacency in adhering to preventive guidelines. Consequently, vaccination continues to be a crucial intervention to reduce the effects of this pandemic. This study investigated the impact of preventive measures and COVID-19 vaccination on the infection, medication, and hospitalization. A cross-sectional online survey was conducted between 23 December 2021 and 12 March 2022 in Algeria. To evaluate the effectiveness of strategies aimed at avoiding and minimizing SARS-CoV-2 infection and severity, a questionnaire was created and validated. Descriptive statistics and logistic regression analyses were computed to identify associations between dependent and independent variables. Variables with a p-value of < 0.05 were considered statistically significant. Our results indicated that out of 2294 answers received, only 16% of our sample was vaccinated, and more than 60% did not apply preventive guidelines. As a result, 45% were infected with SARS-CoV-2, 75% took treatment (even preventive), and 9% were hospitalized. The logistic regression showed that the impact of preventive measures on the unvaccinated is statistically not significant (OR: 0.764, 95% CI = 0. 555-1.052; p = 0.09). However, this relationship changes significantly for people who are vaccinated (OR: 0.108, 95% CI = 0.047-0.248; p < 0.0001). Our results also demonstrated that the impact of protective measures on non-vaccinated individuals is statistically significant in reducing the need to receive anti-COVID-19 treatments (OR: 0.447, 95% CI = 0.321-0.623; p < 0.0001). Furthermore, the results showed that the impact of preventive measures on the non-vaccinated population is also statistically significant in reducing the risk of hospitalization (OR: 0.211, 95% CI = 0.081-0.548; p < 0.0001). Moreover, vaccinated individuals who neglect preventive measures must take the COVID-19 medication at a rate of 3.77 times (OR: 3.77) higher than those who follow preventive measures and are vaccinated. In short, our findings demonstrate the importance of combining preventive measures and vaccination in order to fight against the pandemic. Therefore, we advise the Ministry of Health and relevant authorities to put more effort into enhancing public knowledge about the COVID-19 infection and vaccination through education and awareness initiatives. Parallel to implementing vaccination as additional preventive strategy, behavioral change initiatives must be improved to encourage adherence to COVID-19 prevention recommendations.

Prevalence and associated factors of COVID-19 among Moroccan physicians: A cross-sectional study

BACKGROUND

Coronavirus disease (COVID-19) has emerged and spread rapidly worldwide and established a global public health crisis in early 2020. The first Moroccan case was reported on March 2, 2020. Since then, healthcare workers (HCWs) played a major role in saving human lives threatened by COVID-19. This study aimed to assess the prevalence of COVID-19 infection among Moroccan physicians and to report associated risk factors prior vaccination campaign.

METHODS

A cross-sectional study was carried out in the Fez-Meknes region of Morocco, 545 physicians' data was collected using a self-reported online questionnaire. The data collection was done between December 1, 2020, and February 1, 2021.

RESULTS

The prevalence of COVID-19 among physicians was 27.3%. The mean age of the confirmed COVID-19 group was 38.4±12.9 years old. There was no association between COVID-19 infection and preventive measures compliance by physicians and healthcare authorities in the workplace. However, multivariate analysis strengthened the following factors such as increased risk of COVID-19 infection within men ☯aOR:1.896; 95% IC 1.272-2.828; p = 0.002]; the presence of at least one comorbidity ☯aOR:2.268; 95%IC 1.414-3.637; p = 0.001]; and working at a university or military hospitals ☯aOR:2.578; 95%IC 1.667-3.989; p = 0.001].

CONCLUSION

This study allows comparing COVID-19 prevalence among healthcare workers before and after vaccination programs. This should support better preparation strategy for any future pandemics with appropriate and increased awareness for men, carrying comorbidity, and working environment with high COVID-19 disease management.

Short-Term Adverse Effects Following Booster Dose of Inactivated-Virus vs. Adenoviral-Vector COVID-19 Vaccines in Algeria: A Cross-Sectional Study of the General Population

COVID-19 booster vaccines have been adopted in almost all countries to enhance the immune response and combat the emergence of new variants. Algeria adopted this strategy in November 2021. This study was conducted to consider the self-reported side effects of COVID-19 booster vaccines by Algerians who were vaccinated with a booster dose of one of the approved inactivated-virus vaccines, such as BBIBP-CorV and CoronaVac, or one of the adenoviral-vector-based vaccines, such as Gam-COVID-Vac, AZD1222 and Ad26.COV2.S, and to determine the eventual risk factors. A cross-sectional study using an online self-administered questionnaire (SAQ) was conducted in Algeria between 28 April 2022, and 20 July 2022. A descriptive analysis of the 196 individuals who were included showed a nearly equal distribution of adenoviral- (52%) and inactivated-virus vaccines (48%) and of males (49.5%) and females (50.5%). The results showed that 74.7% of the studied population reported at least one local or systemic side effect. These side effects were more frequent among adenoviral-vector vaccinees (87.3%) than inactivated-virus vaccinees (60.6%) (sig. < 0.001). Injection site pain (40.3%), heat at the injection site (21.4%), and arm pain (16.3%) were the most common local side effects. These signs generally appeared in the first 12 h (73.3%) and generally lasted less than 24 h (32.8%). More interestingly, these signs differed from those that followed the administration of primer doses (48.5%) and were generally more severe (37%). The same observation was reported for systemic side effects, where the signs were especially most severe in the adenoviral-vaccinated group (49.4% vs. 20.8%; sig. = 0.001). These signs generally appeared within the first day (63.6%) and mostly disappeared before two days (50.8%), with fatigue (41.8%), fever (41.3%), and headache (30.1%) being the most common. Adenoviral-vector vaccinees (62.7%) were more likely to use medications to manage these side effects than were inactivated-virus vaccinees (45.7%) (sig. = 0.035) and paracetamol (48.5%) was the most used medication. Adenoviral-based vaccines were the types of vaccines that were most likely to cause side effects. In addition, being female increased the risk of developing side effects; regular medication was associated with local side effects among inactivated-virus vaccinees; and previous infection with COVID-19 was associated with systemic and local side effects among adenovirus-based vaccinees. These results support the short-term safety of booster vaccines, as has been reported for primer doses.

A Statistical Model of COVID-19 Infection Incidence in the Southern Indian State of Tamil Nadu

In this manuscript, we present an analysis of COVID-19 infection incidence in the Indian state of Tamil Nadu. We used seroprevalence survey data along with COVID-19 fatality reports from a six-month period (1 June 2020 to 30 November 2020) to estimate age- and sex-specific COVID-19 infection fatality rates (IFR) for Tamil Nadu. We used these IFRs to estimate new infections occurring daily using the daily COVID-19 fatality reports published by the Government of Tamil Nadu. We found that these infection incidence estimates for the second COVID wave in Tamil Nadu were broadly consistent with the infection estimates from seroprevalence surveys. Further, we propose a composite statistical model that pairs a k-nearest neighbours model with a power-law characterisation for "out-of-range" extrapolation to estimate the COVID-19 infection incidence based on observed cases and test positivity ratio. We found that this model matched closely with the IFR-based infection incidence estimates for the first two COVID-19 waves for both Tamil Nadu as well as the neighbouring state of Karnataka. Finally, we used this statistical model to estimate the infection incidence during the recent "Omicron wave" in Tamil Nadu and Karnataka.