Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020

Cycle threshold (Ct)values of SARS-CoV2 vary with the type of symptoms and comorbidities of the patients

BACKGROUND

Literature depicts no association of cycle threshold values for SARS-CoV-2 with the patient's demographics or clinical history. However, the severity of the COVID-19 symptoms showed some associations with Ct values in previous studies. We aimed to study the relationship of Ct values with type of the symptoms and comorbidities of the patients.

METHODS AND RESULTS

We examined the individuals (n = 8660) that consulted a private diagnostic center for COVID-19 testing. Nasopharyngeal samples were collected from the patients and SARS-CoV-2 was detected by RT-PCR assays using Sansure Biotech kit. Clinical and demographic information was collected by the attending healthcare professional. The differences between groups were analyzed by t-test (unpaired). P-values < 0.05 were considered statistically significant. We found that the mean age of asymptomatic patients (41.47 ± 17.01 years) and symptomatic patients (47.75 ± 17.51) varies significantly (p value < 0.0001). Among the most prevalent symptoms were fever (77.74%) and cough (66.46%). The Ct values of COVID-19 patients with symptoms (25.70 for Orf1ab, 24.25 for N gene) were significantly lower than the Ct values of the patients without symptoms (28.99 for Orf1ab, 28.17 for N gene) (p value < 0.0001). Patients having co-occurrence of cough with fever (24.67 for Orf1ab, 22.86 for N gene) and solely fever (25.12 for Orf1ab, 24.01 for N gene) showed significantly lower Ct values than the patients without symptoms (28.99 for Orf1ab, 28.17 for N gene, p value < 0.0001). However, this difference was independent of patients sex and dependent upon patient's age and SARS-CoV-2 detected gene. The most prevalent comorbidities among COVID-19 patients were blood pressure (64.7%) and diabetes (50.5%). The patients having blood pressure and diabetes together showed significantly lower Ct values (23.37 for Orf1ab, 23.33 for N gene) than patients without comorbidities (27.65 for Orf1ab, 26.75 for N gene, p value < 0.001).

CONCLUSION

We conclude that the Ct values of the SARS-CoV-2 detected gene (either Orf1ab or N) is associated with the types of symptoms and comorbidities of the COVID-19 patients. Furthermore, the relationship between Ct values and symptomology or comorbidity of the patients is independent of patient's sex but dependent upon patient's age and SARS-CoV-2 detected gene.

Impact of COVID-19 vaccines in patients on hemodialysis: an Italian multicentre cohort study

BACKGROUND

The aim of this study was to evaluate the impact on the national health system of COVID-19 infection in vaccinated patients undergoing haemodialysis.

METHODS

From the cohort of vaccinated dialysis patients enrolled in 118 dialysis centres, we calculated hospitalisation incidence in COVID-19-infected subjects. COVID-19-related hospitalisations and ICU admissions were analysed over two time periods (prior to administration of the third dose and following administration of the third dose of vaccine) and adjusted for several co-variates. Using the general population as the reference, we then calculated the Standardized Incidence Ratio (SIR) of hospitalisation.

RESULTS

Eighty-two subjects out of 1096 infected patients were hospitalised (7.5%) and sixty-four hospitalisations occurred among the 824 infected persons after the third dose. Age ≥ 60 years (Adj RR 2.91; 95% CI 1.34-6.30) and lung disease (Adj RR = 2.45; 95% CI 1.32-4.54) were the only risk factors associated with hospitalisation. The risk of ICU admission in the second time period (Time 2) was reduced by 86% (RR = 0.14; 95% CI 0.03-0.71) compared to the first time period (Time 1). The SIR of hospitalisation (SIR 14.51; 95% CI 11.37-17.65) and ICU admission (SIR 14.58; 95% CI 2.91-26.24) showed an increase in the number of events in dialysis patients compared to the general population.

CONCLUSIONS

Our analysis revealed that while the second variant of the virus increased infection rates, it was concurrently associated with mitigated severity of infections. Dialysis patients exhibited a higher susceptibility to both COVID-19 hospitalisation and ICU admission than the general population throughout the pandemic.

Spatial spread of COVID-19 during the early pandemic phase in Italy

Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. Results are validated using a multi-patch dynamic transmission model reproducing the spatiotemporal distribution of identified cases. Our results show that the contribution of short-distance ( ≤ 10 k m ) transmission increased from less than 40% in the last week of January to more than 80% in the first week of March 2020. On March 7, 2020, that is the day before the first regional lockdown was imposed, more than 200 local transmission foci were contributing to the spread of SARS-CoV-2 in Italy. At the time, isolation measures imposed only on municipalities with at least ten ascertained cases would have left uncontrolled more than 75% of spillover transmission from the already affected municipalities. In early March, national-wide restrictions were required to curb short-distance transmission of SARS-CoV-2 in Italy.

Ecological comparison of six countries in two waves of COVID-19

Objective

The purpose of this study is to provide experience and evidence support for countries to deal with similar public health emergencies such as COVID-19 by comparing and analyzing the measures taken by six countries in epidemic prevention and control.

Methods

This study extracted public data on COVID-19 from the official website of various countries and used ecological comparative research methods to compare the specific situation of indicators such as daily tests per thousand people, stringency index, and total vaccinations per hundred people in countries.

Results

The cumulative death toll in China, Germany and Australia was significantly lower than that in the United States, South Africa and Italy. Expanding the scale of testing has helped control the spread of the epidemic to some extent. When the epidemic situation is severe, the stringency index increases, and when the epidemic situation tends to ease, the stringency index decreases. Increased vaccination rates, while helping to build an immune barrier, still need to be used in conjunction with non-drug interventions.

Conclusion

The implementation of non-drug interventions and vaccine measures greatly affected the epidemic prevention and control effect. In responding to public health emergencies such as the COVID-19 epidemic, countries should draw on international experience, closely align with their national conditions, follow the laws of epidemiology, actively take non-drug intervention measures, and vigorously promote vaccine research and development and vaccination.

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.

Mortality rates from asbestos-related diseases in Italy during the first year of the COVID-19 pandemic

Background and aim

Patients with interstitial lung diseases, including asbestosis, showed high susceptibility to the SARS-CoV-2 virus and a high risk of severe COVID-19 symptoms. Italy, highly impacted by asbestos-related diseases, in 2020 was among the European countries with the highest number of COVID-19 cases. The mortality related to malignant mesotheliomas and asbestosis in 2020 and its relationship with COVID-19 in Italy are investigated.

Methods

All death certificates involving malignant mesotheliomas or asbestosis in 2010-2020 and those involving COVID-19 in 2020 were retrieved from the National Registry of Causes of Death. Annual mortality rates and rate ratios (RRs) of 2020 and 2010-2014 compared to 2015-2019 were calculated. The association between malignant pleural mesothelioma (MPM) and asbestosis with COVID-19 in deceased adults ≥80 years old was evaluated through a logistic regression analysis (odds ratios: ORs), using MPM and asbestosis deaths COVID-19-free as the reference group. The hospitalization for asbestosis in 2010-2020, based on National Hospital Discharge Database, was analyzed.

Results

In 2020, 746,343 people died; out of them, 1,348 involved MPM and 286 involved asbestosis. Compared to the period 2015-2019, the mortality involving the two diseases decreased in age groups below 80 years; meanwhile, an increasing trend was observed in subjects aged 80 years and older, with a relative mortality risks of 1.10 for MPM and 1.17 for asbestosis. In subjects aged ≥80 years, deaths with COVID-19 were less likely to have MPM in both genders (men: OR = 0.22; women: OR = 0.44), while no departure was observed for asbestosis. A decrease in hospitalization in 2020 with respect to those in 2010-2019 in all age groups, both considering asbestosis as the primary or secondary diagnosis, was observed.

Conclusions

The increasing mortality involving asbestosis and, even if of slight entity, MPM, observed in people aged over 80 years during the 1st year of the COVID-19 pandemic, aligned in part with the previous temporal trend, could be due to several factors. Although no positive association with COVID-19 mortality was observed, the decrease in hospitalizations for asbestosis among individuals aged over 80 years, coupled with the increase in deaths, highlights the importance of enhancing home-based assistance during the pandemic periods for vulnerable patients with asbestos-related conditions.

COVID-19 vaccination effectiveness in the population of Friuli Venezia Giulia, North-East Italy. Control of bias associated with divergent compliance to policies in a test-negative case-control study

BACKGROUND

Vaccine effectiveness (VE) studies consolidate knowledge of real-world effectiveness in different contexts. However, methodological issues may undermine their conclusions: to assess the VE against COVID-19 within the Italian population, a specific threat to validity is related to the consequences of divergent compliance to the Green Pass policy.

METHODS

To address this challenge we conducted a test negative case-control (TNCC) study and multiple sensitivity analysis among residents aged ≥ 12 in Friuli Venezia Giulia Region (FVG), North-east Italy, from February 1, 2021 to March 31, 2022. Information regarding 211,437 cases of COVID-19 infection and 845,748 matched controls was obtained from the regional computerized health database. The investigation considered: COVID-19 infection, hospitalization, and death. Multiple conditional logistic regressions adjusted for covariates were performed and VE was estimated as (1-OR COVID-19vaccinated vs. unvaccinated)x100. Mediation analyses were carried out to offset potential collider variables, particularly, the number of swabs performed after the introduction of pandemic restrictions.

RESULTS

Full-cycle VE against infection decreased from 96% (95% CI: 96, 97) in the Alpha period to 43% (95% CI: 42, 45) in the Omicron period. Booster dose raised the protection in Omicron period to 67% (95% CI: 66, 67). Against the evasive Omicron variant, the protection of the booster dose was 87% (95% CI: 83, 90) for hospitalization and 90% (95% CI: 82, 95) for death. The number of swabs performed was included as a covariate in the adjustments, and the mediation analysis confirmed that it was a strong mediator between vaccination and COVID-19-related outcomes.

CONCLUSIONS

The study suggests that, under similar TNCC settings, mediation analysis and adjustment for number of diagnostic tests should be included, as an effective approach to the challenge of differential testing behavior that may determine substantial selection bias. This correction allowed us to align with results from other studies that show how full-cycle VE against infection was initially high but decreased over time by variant circulation, counterbalanced by booster dose that raised protection across variants and outcome severity.

Estimated Effectiveness of a Primary Cycle of Protein Recombinant Vaccine NVX-CoV2373 Against COVID-19

Importance

Protein recombinant vaccine NVX-CoV2373 (Novavax) against COVID-19 was authorized for its use in adults in late 2021, but evidence on its estimated effectiveness in a general population is lacking.

Objective

To estimate vaccine effectiveness of a primary cycle with NVX-CoV2373 against SARS-CoV-2 infection and symptomatic COVID-19.

Design, Setting, and Participants

Retrospective cohort study linking data from the national vaccination registry and the COVID-19 surveillance system in Italy during a period of Omicron predominance. All adults starting a primary vaccination with NVX-CoV2373 between February 28 and September 4, 2022, were included, with follow-up ending on September 25, 2022. Data were analyzed in February 2023.

Exposures

Partial (1 dose only) vaccination and full vaccination (2 doses) with NVX-CoV-2373.

Main Outcomes and Measures

Notified SARS-CoV-2 infection and symptomatic COVID-19. Poisson regression models were used to estimate effectiveness against both outcomes. Adjusted estimated vaccine effectiveness was calculated as (1 - incidence rate ratio) × 100.

Results

The study included 20 903 individuals who started the primary cycle during the study period. Median (IQR) age of participants was 52 (39-61) years, 10 794 (51.6%) were female, and 20 592 participants (98.5%) had no factors associated with risk for severe COVID-19. Adjusted estimated vaccine effectiveness against notified SARS-CoV-2 infection in those partially vaccinated with NVX-CoV2373 was 23% (95% CI, 13%-33%) and was 31% (95% CI, 22%-39%) in those fully vaccinated. Estimated vaccine effectiveness against symptomatic COVID-19 was 31% (95% CI, 16%-44%) in those partially vaccinated and 50% (95% CI, 40%-58%) in those fully vaccinated. Estimated effectiveness during the first 4 months after completion of the primary cycle decreased against SARS-CoV-2 infection but remained stable against symptomatic COVID-19.

Conclusions and Relevance

This cohort study found that, in an Omicron-dominant period, protein recombinant vaccine NVX-CoV2373 was associated with protection against SARS-CoV-2 infection and symptomatic COVID-19. The use of this vaccine could remain an important element in reducing the impact of the SARS-CoV-2 pandemic.

COVID-19 cluster identification and support vector machine classifier model construction using global healthcare and socio-economic features

Coronaviruses of the human variety have been the culprit of global epidemics of varying levels of lethality, including COVID-19, which has impacted more than 200 countries and resulted in 5.7 million fatalities as of May 2022. Effective clinical management necessitates the allocation of sufficient resources and the employment of appropriately skilled personnel. The elderly population and individuals with diabetes are at increased risk of more severe manifestations of COVID-19. Countries with a higher gross domestic product (GDP) typically exhibit superior health outcomes and reduced mortality rates. Here, we suggest a predictive model for the density of medical doctors and nursing personnel for 134 countries using a support vector machine (SVM). The model was trained in 107 countries and tested in 27, with promising results shown by the kappa statistics and ROC analysis. The SVM model used for predictions showed promising results with a high level of agreement between actual and predicted cluster values.

Social Mixing and Network Characteristics of COVID-19 Patients Before and After Widespread Interventions: A Population-based Study

SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9 to 62.8 ; the average shortest path length decreased from 1.53 to 1.14 ; the average betweenness reduced from 0.65 to 0.11 ; the average cluster size dropped from 4.05 to 2.72 ; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.

The Penn Medicine COVID-19 Therapeutics Committee-Reflections on a Model for Rapid Evidence Review and Dynamic Practice Recommendations During a Public Health Emergency

The Penn Medicine COVID-19 Therapeutics Committee-an interspecialty, clinician-pharmacist, and specialist-front line primary care collaboration-has served as a forum for rapid evidence review and the production of dynamic practice recommendations during the 3-year coronavirus disease 2019 public health emergency. We describe the process by which the committee went about its work and how it navigated specific challenging scenarios. Our target audiences are clinicians, hospital leaders, public health officials, and researchers invested in preparedness for inevitable future threats. Our objectives are to discuss the logistics and challenges of forming an effective committee, undertaking a rapid evidence review process, aligning evidence-based guidelines with operational realities, and iteratively revising recommendations in response to changing pandemic data. We specifically discuss the arc of evidence for corticosteroids; the noble beginnings and dangerous misinformation end of hydroxychloroquine and ivermectin; monoclonal antibodies and emerging viral variants; and patient screening and safety processes for tocilizumab, baricitinib, and nirmatrelvir-ritonavir.

Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK

Agent-based models have been proven to be quite useful in understanding and predicting the SARS-CoV-2 virus-originated COVID-19 infection. Person-to-person contact was considered as the main mechanism of viral transmission in these models. However, recent understanding has confirmed that airborne transmission is the main route to infection spread of COVID-19. We have developed a computationally efficient agent-based hybrid model to study the aerial propagation of the virus and subsequent spread of infection. We considered virus, a continuous variable, spreads diffusively in air and members of populations as discrete agents possessing one of the eight different states at a particular time. The transition from one state to another is probabilistic and age linked. Recognizing that population movement is a key aspect of infection spread, the model allows unbiased movement of agents. We benchmarked the model to recapture the temporal stochastic infection count data of the UK. The model investigates various key factors such as movement, infection susceptibility, new variants, recovery rate and duration, incubation period and vaccination on the infection propagation over time. Furthermore, the model was applied to capture the infection spread in Italy and France.

The Effects of the COVID-19-induced Lockdown on the Social Capital and Cultural Capital in Italy

The present study investigated the effects of the first COVID-19 lockdown on the Cultural and Social Capitals in Italy in a large group of adults (n = 1125). The relationships between the COVID-19 spread and participants' Cultural Capital, Social Capital, educational level, occupational prestige, and age were studied using structural equation models. For women but not for men, pandemic spread was positively affected by occupational prestige and it had a positive relationship with their Social Capital (women: CFI = 0.949; RMSEA = 0.059 [CI = 0.045-0.075]; men: CFI = 0.959; RMSEA = 0.064 [CI = 0.039-0.087]). Moreover, the participants were divided into three validated clusters based on their Cultural and Social Capitals levels to investigate changes in the Capitals compared with the pre-lockdown period. It was found that the lockdown contributed to improving the gap among individuals increasing high levels and decreasing low levels of both the Capitals. People with high Cultural and Social Capitals seemed to have seized the opportunity given by COVID-19 restrictions to cultivate their cultural interests and become more involved within their networks. In contrast, individuals with low Cultural and Social Capitals paid the highest price for the social isolation. Given that the Capitals encourage healthy behavior and influence well-being and mental health, institutions should develop or improve their policies and practices to foster individual resources, and make fairer opportunities available during the pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11205-023-03140-7.

Initial characteristics and course of disease in patients with suspected COVID-19 managed in general practice: a prospective, multicentre cohort study

OBJECTIVES

To describe and compare the initial clinical characteristics of a cohort of patients with suspected COVID-19 managed by general practitioners (GPs); to assess whether 3-month persistent symptoms were more frequent among confirmed cases than among no-COVID cases; and to identify factors predictive of persistent symptoms and adverse outcomes among confirmed cases.

DESIGN AND SETTING

A comparative, prospective, multicentre cohort study in primary care in the Paris region of France.

PARTICIPANTS

521 patients aged ≥18 with suspected COVID-19 were enrolled between March and May 2020.

OUTCOME MEASURES

Initial symptoms, COVID-19 status, persistent symptoms 3 months after inclusion and a composite criterion for potentially COVID-19-related events (hospitalisation, death, emergency department visits). The final COVID-19 status ('confirmed', 'no-COVID' and 'uncertain' cases) was determined by the GP after the receipt of the laboratory test results.

RESULTS

516 patients were analysed; 166 (32.2%) were classified into the 'confirmed COVID' group, 180 (34.9%) into the 'no-COVID' group and 170 (32.9%) in the 'uncertain COVID' group. Confirmed cases were more likely to have persistent symptoms than no-COVID cases (p=0.09); initial fever/feeling feverish and anosmia were independently associated with persistent symptoms. At 3 months, we observed 16 (9.8%) COVID-19-related hospital admissions, 3 (1.8%) intensive care unit admissions, 13 (37.1%) referrals to an emergency department and no death. Age >70 and/or at least one comorbidity (OR 6.53; 95% CI 1.13-37.84; p=0.036), abnormalities in a lung examination (15.39; 95% CI 1.61-146.77; p=0.057) and two or more systemic symptoms (38.61; 95% CI 2.30-647.40; p=0.011) were associated with the composite criterion.

CONCLUSIONS

Although most patients with COVID-19 in primary care had mild disease with a benign course, almost one in six had persistent symptoms at 3 months. These symptoms were more frequent in the 'confirmed COVID' group. Our findings need to be confirmed in a prospective study with longer follow-up.

Impact of tiered measures on social contact and mixing patterns of in Italy during the second wave of COVID-19

BACKGROUND

Most countries around the world enforced non-pharmaceutical interventions against COVID-19. Italy was one of the first countries to be affected by the pandemic, imposing a hard lockdown, in the first epidemic wave. During the second wave, the country implemented progressively restrictive tiers at the regional level according to weekly epidemiological risk assessments. This paper quantifies the impact of these restrictions on contacts and on the reproduction number.

METHODS

Representative (with respect to age, sex, and region of residence) longitudinal surveys of the Italian population were undertaken during the second epidemic wave. Epidemiologically relevant contact patterns were measured and compared with pre-pandemic levels and according to the level of interventions experienced by the participants. Contact matrices were used to quantify the reduction in the number of contacts by age group and contact setting. The reproduction number was estimated to evaluate the impact of restrictions on the spread of COVID-19.

RESULTS

The comparison with the pre-pandemic baseline shows a significant decrease in the number of contacts, independently from the age group or contact settings. This decrease in the number of contacts significantly depends on the strictness of the non-pharmaceutical interventions. For all levels of strictness considered, the reduction in social mixing results in a reproduction number smaller than one. In particular, the impact of the restriction on the number of contacts decreases with the severity of the interventions.

CONCLUSIONS

The progressive restriction tiers implemented in Italy reduced the reproduction number, with stricter interventions associated with higher reductions. Readily collected contact data can inform the implementation of mitigation measures at the national level in epidemic emergencies to come.

A Markovian model for the spread of the SARS-CoV-2 virus

We propose a Markovian stochastic approach to model the spread of a SARS-CoV-2-like infection within a closed group of humans. The model takes the form of a Partially Observable Markov Decision Process (POMDP), whose states are given by the number of subjects in different health conditions. The model also exposes the different parameters that have an impact on the spread of the disease and the various decision variables that can be used to control it (e.g, social distancing, number of tests administered to single out infected subjects). The model describes the stochastic phenomena that underlie the spread of the epidemic and captures, in the form of deterministic parameters, some fundamental limitations in the availability of resources (hospital beds and test swabs). The model lends itself to different uses. For a given control policy, it is possible to verify if it satisfies an analytical property on the stochastic evolution of the state (e.g., to compute probability that the hospital beds will reach a fill level, or that a specified percentage of the population will die). If the control policy is not given, it is possible to apply POMDP techniques to identify an optimal control policy that fulfils some specified probabilistic goals. Whilst the paper primarily aims at the model description, we show with numeric examples some of its potential applications.

Long-Term Exposure to Ambient Air Pollution and Mortality among Four Million COVID-19 Cases in Italy: The EpiCovAir Study

BACKGROUND

The role of chronic exposure to ambient air pollutants in increasing COVID-19 fatality is still unclear.

OBJECTIVES

The study aimed to investigate the association between long-term exposure to air pollutants and mortality among 4 million COVID-19 cases in Italy.

METHODS

We obtained individual records of all COVID-19 cases identified in Italy from February 2020 to June 2021. We assigned 2016-2019 mean concentrations of particulate matter (PM) with aerodynamic diameter ≤10μm (PM10), PM with aerodynamic diameter ≤2.5μm (PM2.5), and nitrogen dioxide (NO2) to each municipality (n=7,800) as estimates of chronic exposures. We applied a principal component analysis (PCA) and a generalized propensity score (GPS) approach to an extensive list of area-level covariates to account for major determinants of the spatial distribution of COVID-19 case-fatality rates. Then, we applied generalized negative binomial models matched on GPS, age, sex, province, and month. As additional analyses, we fit separate models by pandemic periods, age, and sex; we quantified the numbers of COVID-19 deaths attributable to exceedances in annual air pollutant concentrations above predefined thresholds; and we explored associations between air pollution and alternative outcomes of COVID-19 severity, namely hospitalizations or accesses to intensive care units.

RESULTS

We analyzed 3,995,202 COVID-19 cases, which generated 124,346 deaths. Overall, case-fatality rates increased by 0.7% [95% confidence interval (CI): 0.5%, 0.9%], 0.3% (95% CI: 0.2%, 0.5%), and 0.6% (95% CI: 0.5%, 0.8%) per 1 μg/m3 increment in PM2.5, PM10, and NO2, respectively. Associations were higher among elderly subjects and during the first (February 2020-June 2020) and the third (December 2020-June 2021) pandemic waves. We estimated ∼8% COVID-19 deaths were attributable to pollutant levels above the World Health Organization 2021 air quality guidelines.

DISCUSSION

We found suggestive evidence of an association between long-term exposure to ambient air pollutants with mortality among 4 million COVID-19 cases in Italy. https://doi.org/10.1289/EHP11882.

Priority age targets for COVID-19 vaccination in Ethiopia under limited vaccine supply

The worldwide inequitable access to vaccination claims for a re-assessment of policies that could minimize the COVID-19 burden in low-income countries. Nine months after the launch of the national vaccination program in March 2021, only 3.4% of the Ethiopian population received two doses of COVID-19 vaccine. We used a SARS-CoV-2 transmission model to estimate the level of immunity accrued before the launch of vaccination in the Southwest Shewa Zone (SWSZ) and to evaluate the impact of alternative age priority vaccination targets in a context of limited vaccine supply. The model was informed with available epidemiological evidence and detailed contact data collected across different geographical settings (urban, rural, or remote). We found that, during the first year of the pandemic, the mean proportion of critical cases occurred in SWSZ attributable to infectors under 30 years of age would range between 24.9 and 48.0%, depending on the geographical setting. During the Delta wave, the contribution of this age group in causing critical cases was estimated to increase on average to 66.7-70.6%. Our findings suggest that, when considering the vaccine product available at the time (ChAdOx1 nCoV-19; 65% efficacy against infection after 2 doses), prioritizing the elderly for vaccination remained the best strategy to minimize the disease burden caused by Delta, irrespectively of the number of available doses. Vaccination of all individuals aged ≥ 50 years would have averted 40 (95%PI: 18-60), 90 (95%PI: 61-111), and 62 (95%PI: 21-108) critical cases per 100,000 residents in urban, rural, and remote areas, respectively. Vaccination of all individuals aged ≥ 30 years would have averted an average of 86-152 critical cases per 100,000 individuals, depending on the setting considered. Despite infections among children and young adults likely caused 70% of critical cases during the Delta wave in SWSZ, most vulnerable ages should remain a key priority target for vaccination against COVID-19.

The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave

The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m² showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.

Reduction of the risk of severe COVID-19 due to Omicron compared to Delta variant in Italy (November 2021 - February 2022)

OBJECTIVES

During 2022, Omicron became the dominant SARS-CoV-2 variant in Europe. This study aims to assess the impact of such variant on severe disease from SARS-CoV-2 compared with the Delta variant in Italy.

METHODS

Using surveillance data, we assessed the risk of developing severe COVID-19 with Omicron infection compared with Delta in individuals aged ≥12 years using a multilevel negative binomial model adjusting for sex, age, vaccination status, occupation, previous infection, weekly incidence, and geographical area. We also analyzed the interaction between the sequenced variant, age, and vaccination status.

RESULTS

We included 21,645 cases of SARS-CoV-2 infection where genome sequencing found Delta (10,728) or Omicron (10,917), diagnosed from November 15, 2021 to February 01, 2022. Overall, 3,021 cases developed severe COVID-19. We found that Omicron cases had a reduced risk of severe COVID-19 compared with Delta cases (incidence rate ratio [IRR] = 0.77; 95% confidence interval [CI]: 0.70-0.86). The largest difference was observed in cases aged 40-59 (IRR = 0.66; 95% CI: 0.55-0.79), while no protective effect was found in those aged 12-39 (IRR = 1.03; 95% CI: 0.79-1.33). Vaccination was associated with a lower risk of developing severe COVID-19 in both variants.

CONCLUSION

The Omicron variant is associated with a lower risk of severe COVID-19 compared to infection with the Delta variant, but the degree of protection varies with age.

The medium-term perceived impact of work from home on life and work domains of knowledge workers during COVID-19 pandemic: A survey at the National Research Council of Italy

Objective

The study aimed to investigate perceptions and determinants of the overall impact on life and work domains among a community of knowledge workers after 18 months of forced work from home due to the pandemic.

Methods

A cross-sectional study with a retrospective assessment was conducted early in 2022 at the National Research Council of Italy. Five single-item questions explored the perceived impact on life domain while a 7-item scale the impact on the work domain. Bivariate analyses and multivariate regressions were used to evaluate the associations between impacts and some key factors defined by 29 ad hoc closed questions.

Results

More than 95% of the 748 respondents reported a perceived change in at least one item of the life domain. For each of these items, although a large group of subjects has reported that working from home had no impact (from 27 to 55%), in the rest of the sample the positive evaluation (from 30 to 60%) clearly prevailed over the negative one. Overall, most of the subjects (64%) rated the impact on the work experience positively. Relationship with colleagues and participation in the work context were the items where the greatest number of negative rates was concentrated (27 and 25%, respectively). On the other hand, positive perceptions prevailed over both negative perceptions and lack of impact perceptions on the subjects of organizational flexibility and quality of work. The frequency of work-room sharing, home-work commute time and changes in sedentary lifestyle, have been identified as common explanatory factors of perceived impacts on both domains.

Conclusion

Overall, respondents reported positive rather than negative perceived impacts of forced work from home in both their lives and work. The obtained results suggest that policies to promote the physical and mental health of employees, strengthen inclusion and maintain a sense of community are necessary to improve workers' health and prevent the effects of perceived isolation on research activities.

Severity Status of COVID-19 and Its Associated Factors at the Nyarugenge Treatment Center in Rwanda

Background The COVID-19 pandemic has continued to be a public health emergency currently; on March 11, 2020, the World Health Organization (WHO) declared it a global pandemic. Despite the Rwanda National Health Measures that have been put in place to protect the public including lockdowns, curfew, face mask mandate, handwashing sensitization, etc., severe morbidity and mortality cases of COVID-19 are continued to be seen. Some studies have linked COVID-19 complications to its direct chain of mechanism; however, other studies have linked comorbidity or underlying disease conditions to its poor prognosis. Studies have not yet been conducted in Rwanda on the severe status of COVID-19 and its associated factors among patients. Therefore, this study aimed to assess the severe status of COVID-19 and its associated factors at the Nyarugenge Treatment Center. Methods A descriptive cross-sectional study was done. All patients admitted to the Nyarugenge Treatment Center from January 8, 2021, when the hospital opened, until the end of May 2021 were recruited in the study. The eligible participants were all patients who were admitted and tested positive for COVID-19 by RT-PCR method according to the Rwanda Ministry of Health criteria. Results All data were analyzed using the Statistical Package for the Social Sciences (SPSS) software, version 25 (IBM Corp., Armonk, NY). The number of patients admitted during the study period was 648, with a median age of 53; 45.2% of them were females, and 54.2% were males. Of these, 81.2% (526) were discharged from the hospital, while 18.8% (122) died. The proportion of severe status of COVID-19 was 42.1%. The factors that showed a risk of severe COVID-19 status were age and the number of comorbidities. Patients aged above 60 years (OR = 11.7, 95% CI: 5.35-25.67, p-value < 0.001) and those between the age of 51 and 60 (OR = 6.86, 95% CI: 2.96-15.93, p-value < 0.001) were 12 and seven times more likely to have severe COVID-19 status compared to those aged below 30 years. Having two comorbidities had twice the risk of developing a severe COVID-19 status compared to those with no comorbidity (OR = 2.13, 95% CI: 1.20-3.77, p-value < 0.001). Conclusion Elderly people and those with comorbidities are encouraged to obtain all standard operating procedures and comply with the vaccination program.

SARS-CoV2 Infection and Comorbidity in Inmates: A Study of Central Italy

BACKGROUND AND OBJECTIVE

The presence of multiple chronic diseases is associated with an increase in mortality when related to COVID-19 infection.

THE AIMS OF OUR STUDY WERE

(i) to evaluate the association between the severity of the COVID-19 disease, defined as symptomatic hospitalized in prison or symptomatic hospitalized out of prison, and the presence of one or more comorbidities in two prisons in central Italy: L'Aquila and Sulmona; (ii) to describe the profiles of inmates using multiple correspondence analysis (MCA).

METHODS

A database was created including age, gender and clinical variables. The database containing anonymized data was password-protected. The Kruskal-Wallis test was used to evaluate a possible association between diseases and the severity of COVID-19 stratified by age groups. We used MCA to describe a possible characteristic profile of inmates.

RESULTS

Our results show that in the 25-50-year-old age group (COVID-19-negative) in the L'Aquila prison, 19/62 (30.65%) were without comorbidity, 17/62 (27.42%) had 1-2 comorbidities and only 3.23% had >2 diseases. It is interesting to note that in the elderly group, the frequency of 1-2 or >2 pathologies was higher than in the younger group, and only 3/51 (5.88%) inmates did not have comorbidities and were COVID-19 negative (p = 0.008). The MCA identified the following profiles: the prison of L'Aquila showed a group of women over 60 with diabetes, cardiovascular and orthopedic problems, and hospitalized for COVID-19; the Sulmona prison presented a group of males over 60 with diabetes, cardiovascular, respiratory, urological, gastrointestinal and orthopedic problems, and hospitalized or symptomatic due to COVID-19.

CONCLUSIONS

our study has demonstrated and confirmed that advanced age and the presence of concomitant pathologies have played a significant role in the severity of the disease: symptomatic hospitalized in the prison; symptomatic hospitalized out of the prison.

Assessment of Excess Mortality in Italy in 2020-2021 as a Function of Selected Macro-Factors

BACKGROUND

Excess mortality (EM) can reliably capture the impact of a pandemic, this study aims at assessing the numerous factors associated with EM during the COVID-19 pandemic in Italy.

METHODS

Mortality records (ISTAT 2015-2021) aggregated in the 610 Italian Labour Market Areas (LMAs) were used to obtain the EM P-scores to associate EM with socioeconomic variables. A two-step analysis was implemented: (1) Functional representation of EM and clustering. (2) Distinct functional regression by cluster.

RESULTS

The LMAs are divided into four clusters: 1 low EM; 2 moderate EM; 3 high EM; and 4 high EM-first wave. Low-Income showed a negative association with EM clusters 1 and 4. Population density and percentage of over 70 did not seem to affect EM significantly. Bed availability positively associates with EM during the first wave. The employment rate positively associates with EM during the first two waves, becoming negatively associated when the vaccination campaign began.

CONCLUSIONS

The clustering shows diverse behaviours by geography and time, the impact of socioeconomic characteristics, and local governments and health services' responses. The LMAs allow to draw a clear picture of local characteristics associated with the spread of the virus. The employment rate trend confirmed that essential workers were at risk, especially during the first wave.

Minimising school disruption under high incidence conditions due to the Omicron variant in France, Switzerland, Italy, in January 2022

BackgroundAs record cases of Omicron variant were registered in Europe in early 2022, schools remained a vulnerable setting undergoing large disruption.AimThrough mathematical modelling, we compared school protocols of reactive screening, regular screening, and reactive class closure implemented in France, in Baselland (Switzerland), and in Italy, respectively, and assessed them in terms of case prevention, testing resource demand, and schooldays lost.MethodsWe used a stochastic agent-based model of SARS-CoV-2 transmission in schools accounting for within- and across-class contacts from empirical contact data. We parameterised it to the Omicron BA.1 variant to reproduce the French Omicron wave in January 2022. We simulated the three protocols to assess their costs and effectiveness for varying peak incidence rates in the range experienced by European countries.ResultsWe estimated that at the high incidence rates registered in France during the Omicron BA.1 wave in January 2022, the reactive screening protocol applied in France required higher test resources compared with the weekly screening applied in Baselland (0.50 vs 0.45 tests per student-week), but achieved considerably lower control (8% vs 21% reduction of peak incidence). The reactive class closure implemented in Italy was predicted to be very costly, leading to > 20% student-days lost.ConclusionsAt high incidence conditions, reactive screening protocols generate a large and unplanned demand in testing resources, for marginal control of school transmissions. Comparable or lower resources could be more efficiently used through weekly screening. Our findings can help define incidence levels triggering school protocols and optimise their cost-effectiveness.

Exploring Italian healthcare facilities response to COVID-19 pandemic: Lessons learned from the Italian Response to COVID-19 initiative

The COVID-19 pandemic exerted an extraordinary pressure on the Italian healthcare system (Sistema Sanitario Nazionale, SSN), determining an unprecedented health crisis. In this context, a multidisciplinary non-governmental initiative called Italian Response to COVID-19 (IRC-19) was implemented from June 2020 to August 2021 to support the Italian health system through multiple activities aimed to mitigate the effects of the pandemic. The objective of this study was to shed light on the role of NGOs in supporting the SSN during the first pandemic wave by specifically exploring: (1) the main challenges experienced by Italian hospitals and out-of-hospital care facilities and (2) the nature and extent of the IRC-19 interventions specifically implemented to support healthcare facilities, to find out if and how such interventions met healthcare facilities' perceived needs at the beginning of the pandemic. We conducted a cross-sectional study using an interviewer administered 32-item questionnaire among 14 Italian healthcare facilities involved in the IRC-19 initiative. Health facilities' main challenges concerned three main areas: healthcare workers, patients, and facilities' structural changes. The IRC-19 initiative contributed to support both hospital and out-of-hospital healthcare facilities by implementing interventions for staff and patients' safety and flow management and interventions focused on the humanization of care. The support from the third sector emerged as an added value that strengthened the Italian response to the COVID-19 pandemic. This is in line with the Health-Emergency and Disaster Risk Management (H-EDRM) precepts, that call for a multisectoral and multidisciplinary collaboration for an effective disaster management.

A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis

BACKGROUND

Following the recent COVID-19 pandemic, returning to normalcy has become the primary goal of global cities. The key for returning to normalcy is to avoid affecting social and economic activities while supporting precise epidemic control. Estimation models for the spatiotemporal spread of the epidemic at the refined scale of cities that support precise epidemic control are limited. For most of 2021, Hong Kong has remained at the top of the "global normalcy index" because of its effective responses. The urban-community-scale spatiotemporal onset risk prediction model of COVID-19 symptom has been used to assist in the precise epidemic control of Hong Kong.

OBJECTIVE

Based on the spatiotemporal prediction models of COVID-19 symptom onset risk, the aim of this study was to develop a spatiotemporal solution to assist in precise prevention and control for returning to normalcy.

METHODS

Over the years 2020 and 2021, a spatiotemporal solution was proposed and applied to support the epidemic control in Hong Kong. An enhanced urban-community-scale geographic model was proposed to predict the risk of COVID-19 symptom onset by quantifying the impact of the transmission of SARS-CoV-2 variants, vaccination, and the imported case risk. The generated prediction results could be then applied to establish the onset risk predictions over the following days, the identification of high-onset-risk communities, the effectiveness analysis of response measures implemented, and the effectiveness simulation of upcoming response measures. The applications could be integrated into a web-based platform to assist the antiepidemic work.

RESULTS

Daily predicted onset risk in 291 tertiary planning units (TPUs) of Hong Kong from January 18, 2020, to April 22, 2021, was obtained from the enhanced prediction model. The prediction accuracy in the following 7 days was over 80%. The prediction results were used to effectively assist the epidemic control of Hong Kong in the following application examples: identified communities within high-onset-risk always only accounted for 2%-25% in multiple epidemiological scenarios; effective COVID-19 response measures, such as prohibiting public gatherings of more than 4 people were found to reduce the onset risk by 16%-46%; through the effect simulation of the new compulsory testing measure, the onset risk was found to be reduced by more than 80% in 42 (14.43%) TPUs and by more than 60% in 96 (32.99%) TPUs.

CONCLUSIONS

In summary, this solution can support sustainable and targeted pandemic responses for returning to normalcy. Faced with the situation that may coexist with SARS-CoV-2, this study can not only assist global cities in responding to the future epidemics effectively but also help to restore social and economic activities and people's normal lives.

The effect of diabetes on COVID-19 incidence and mortality: Differences between highly-developed-country and high-migratory-pressure-country populations

The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82-0.87) and 0.67 (95% CI 0.46-0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22-1.53 vs. 1.20 95% CI 1.14-1.25; HRs for mortality: 3.96 95% CI 1.82-8.60 vs. 1.71 95% CI 1.50-1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48-79.87 vs. 3.91 95% CI 2.69-5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88-1.12) and mortality (MRR: 0.89 95% CI: 0.49-1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41-2.11 among HDC vs. 1.41 95% CI 0.63-3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

Rising incidence of late stage breast cancer after COVID-19 outbreak. Real-world data from the Italian COVID-DELAY study

Purpose

Methods

Results

Conclusions

•

COVID-19 impacted on breast cancer (BC) diagnoses with a reduction of 25% in 2020.

•

Fewer early-stage BC and more symptomatic patients were diagnosed during 2020.

•

Timing of access to BC diagnosis, staging and treatment has not been affected by COVID-19.

•

Less BC cases were reviewed in multidisciplinary tumor meetings during 2020.

The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review

Non-pharmaceutical interventions, such as school closures and stay-at-home orders, have been implemented around the world to control the spread of SARS-CoV-2. Their effectiveness in improving health-related outcomes has been the subject of numerous empirical studies. However, these studies show fairly large variation among methodologies in use, reflecting the absence of an established methodological framework. On the one hand, variation in methodologies may be desirable to assess the robustness of results; on the other hand, a lack of common standards can impede comparability among studies. To establish a comprehensive overview over the methodologies in use, we conducted a systematic review of studies assessing the effectiveness of non-pharmaceutical interventions between January 1, 2020 and January 12, 2021 (n = 248). We identified substantial variation in methodologies with respect to study setting, outcome, intervention, methodological approach, and effectiveness assessment. On this basis, we point to shortcomings of existing studies and make recommendations for the design of future studies.

Doubling time of infectious diseases

The concept of doubling time has been increasingly used since the onset of the coronavirus disease 2019 (COVID-19) pandemic, but its characteristics are not well understood, especially as applied to infectious disease epidemiology. The present study aims to be a practical guide to monitoring the doubling time of infectious diseases. Via simulation exercise, we clarify the epidemiological characteristics of doubling time, allowing possible interpretations. We show that the commonly believed relationship between the doubling time and intrinsic growth rate in population ecology does not strictly apply to infectious diseases, and derive the correct relationship between the two. We examined the impact of varying (i) the growth rate, (ii) the starting point of counting cumulative number of cases, and (iii) the length of observation on statistical estimation of doubling time. It was difficult to recover values of growth rate from doubling time, especially when the growth rate was small. Starting time period is critical when the statistical estimation of doubling time occurs during the course of an epidemic. The length of observation was critical in determining the overall magnitude of doubling time, and when only the latest 1-2 weeks' data were used, the resulting doubling time was very short, regardless of the intrinsic growth rate r. We suggest that doubling time estimates of infectious disease epidemics should at a minimum be accompanied by descriptions of (i) the starting time at which the cumulative count is initiated and (ii) the length of observation.

EpiBeds: Data informed modelling of the COVID-19 hospital burden in England

The first year of the COVID-19 pandemic put considerable strain on healthcare systems worldwide. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, EpiBeds, which was coupled to a model of the generalised epidemic. In this model, individuals progress through different pathways (e.g. may recover, die, or progress to intensive care and recover or die) and data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow the different clinical pathways, the reproduction number of the generalised epidemic, and to make short-term predictions of hospital bed demand. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland at national and regional scales.

COVID-19, the disease caused by SARS-CoV-2, leads to a high proportion of cases requiring admission to hospital. Coupled with the high burden of infections worldwide, this put substantial pressure on healthcare systems. To enable public health systems to cope with the high levels of demand, forecasting models are vital. These models enable public health managers to plan their workloads accordingly. Here, we developed EpiBeds, which combines an epidemic model with a model for patient flow through hospitals. By fitting this model to data from England, EpiBeds has been used to provide short-term forecasts of hospital admissions and bed demand weekly throughout the COVID-19 pandemic. In this paper, we describe the motivation behind the structure of EpiBeds, how the model is fitted to data, and report the estimates of the key parameters throughout the pandemic. We then evaluate the performance of EpiBeds by comparing generated forecasts to future data points, finding good agreement between the forecasts and data.

Back to school or … back to lockdown? The effects of opening schools on the diffusion of COVID-19 in Italian regions

The opening of schools that coincided with the beginning of fall 2020 and the arrival of the second wave of COVID-19 in continental Europe has fostered significant debate in several countries. Some contributions have suggested that youngsters play a minor role in the spread of the virus, given the specific characteristics of this infection; other scholars have raised concerns about the necessary movement that involves keeping schools open, and the consequent potential spread of the virus. In this study, we focus on the Italian case, an interesting setting in which to test the impact of opening schools on the spread of COVID-19, because of the different dates at which schools have opened in the various Italian provinces, and because of the different rates at which the virus has spread across Italy. Our results suggest that open schools have a positive impact on COVID-19 cases, whose spread occurs between 10 and 14 days after opening. While closing schools or using distance learning have other social and economic consequences, making it necessary for policymakers to adopt a holistic evaluation, it should be taken into account that open schools have an impact on the spread of the pandemic.

Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread

BACKGROUND

During the early stage of the COVID-19 pandemic, many countries implemented non-pharmaceutical interventions (NPIs) to control the transmission of SARS-CoV-2, the causative pathogen of COVID-19. Among those NPIs, stay-at-home and quarantine measures were widely adopted and enforced. Understanding the effectiveness of stay-at-home and quarantine measures can inform decision-making and control planning during the ongoing COVID-19 pandemic and for future disease outbreaks.

METHODS

In this study, we use mathematical models to evaluate the impact of stay-at-home and quarantine measures on COVID-19 spread in four cities that experienced large-scale outbreaks in the spring of 2020: Wuhan, New York, Milan, and London. We develop a susceptible-exposed-infected-removed (SEIR)-type model with components of self-isolation and quarantine and couple this disease transmission model with a data assimilation method. By calibrating the model to case data, we estimate key epidemiological parameters before lockdown in each city. We further examine the impact of stay-at-home and quarantine rates on COVID-19 spread after lockdown using counterfactual model simulations.

RESULTS

Results indicate that self-isolation of susceptible population is necessary to contain the outbreak. At a given rate, self-isolation of susceptible population induced by stay-at-home orders is more effective than quarantine of SARS-CoV-2 contacts in reducing effective reproductive numbers [Formula: see text]. Variation in self-isolation and quarantine rates can also considerably affect the duration of outbreaks, attack rates and peak timing. We generate counterfactual simulations to estimate effectiveness of stay-at-home and quarantine measures. Without these two measures, the cumulative confirmed cases could be much higher than reported numbers within 40 days after lockdown in Wuhan, New York, Milan, and London.

CONCLUSIONS

Our findings underscore the essential role of stay-at-home orders and quarantine of SARS-CoV-2 contacts during the early phase of the pandemic.

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.

Alarming Drop in Early Stage Colorectal Cancer Diagnoses After COVID-19 Outbreak: A Real-World Analysis from the Italian COVID-DELAY Study

BACKGROUND

Coronavirus disease 2019 (COVID-19) has triggered the disruption of health care on a global scale. With Italy tangled up in the pandemic response, oncology care has been largely diverted and cancer screenings suspended. Our multicenter Italian study aimed to evaluate whether COVID-19 has impacted access to diagnosis, staging, and treatment for patients newly diagnosed with colorectal cancer (CRC), compared with pre-pandemic time.

METHODS

All consecutive new CRC patients referred to 8 Italian oncology institutions between March and December 2020 were included. Access rate and temporal intervals between date of symptoms onset, radiological and cytohistological diagnosis, treatment start and first radiological evaluation were analyzed and compared with the same months of 2019.

RESULTS

A reduction (29%) in newly diagnosed CRC cases was seen when compared with 2019 (360 vs 506). New CRC patients in 2020 were less likely to be diagnosed with early stage (stages I-II-III) CRC (63% vs 78%, P < .01). Gender and sidedness were similar regardless of the year. The percentage of tumors with any mutation among BRAF, NRAS, and KRAS genes were significantly different between the 2 years (61% in 2020 vs 50% in 2019, P = .04). Timing of access to cancer diagnosis, staging, and treatment for patients with CRC has not been negatively affected by the pandemic. Significantly shorter temporal intervals were observed between symptom onset and first oncological appointment (69 vs 79 days, P = .01) and between histological diagnosis and first oncological appointment (34 vs 42 days, P < .01) during 2020 compared with 2019. Fewer CRC cases were discussed in multidisciplinary meetings during 2020 (38% vs 50%, P = .01).

CONCLUSIONS

Our data highlight a significant drop in CRC diagnosis after COVID-19, especially for early stage disease. The study also reveals a remarkable setback in the multidisciplinary management of patients with CRC. Despite this, Italian oncologists were able to ensure diagnostic-therapeutic pathways proper operation after March 2020.

Global influenza surveillance systems to detect the spread of influenza-negative influenza-like illness during the COVID-19 pandemic: Time series outlier analyses from 2015-2020

BACKGROUND

Surveillance systems are important in detecting changes in disease patterns and can act as early warning systems for emerging disease outbreaks. We hypothesized that analysis of data from existing global influenza surveillance networks early in the COVID-19 pandemic could identify outliers in influenza-negative influenza-like illness (ILI). We used data-driven methods to detect outliers in ILI that preceded the first reported peaks of COVID-19.

METHODS AND FINDINGS

We used data from the World Health Organization's Global Influenza Surveillance and Response System to evaluate time series outliers in influenza-negative ILI. Using automated autoregressive integrated moving average (ARIMA) time series outlier detection models and baseline influenza-negative ILI training data from 2015-2019, we analyzed 8,792 country-weeks across 28 countries to identify the first week in 2020 with a positive outlier in influenza-negative ILI. We present the difference in weeks between identified outliers and the first reported COVID-19 peaks in these 28 countries with high levels of data completeness for influenza surveillance data and the highest number of reported COVID-19 cases globally in 2020. To account for missing data, we also performed a sensitivity analysis using linear interpolation for missing observations of influenza-negative ILI. In 16 of the 28 countries (57%) included in this study, we identified positive outliers in cases of influenza-negative ILI that predated the first reported COVID-19 peak in each country; the average lag between the first positive ILI outlier and the reported COVID-19 peak was 13.3 weeks (standard deviation 6.8). In our primary analysis, the earliest outliers occurred during the week of January 13, 2020, in Peru, the Philippines, Poland, and Spain. Using linear interpolation for missing data, the earliest outliers were detected during the weeks beginning December 30, 2019, and January 20, 2020, in Poland and Peru, respectively. This contrasts with the reported COVID-19 peaks, which occurred on April 6 in Poland and June 1 in Peru. In many low- and middle-income countries in particular, the lag between detected outliers and COVID-19 peaks exceeded 12 weeks. These outliers may represent undetected spread of SARS-CoV-2, although a limitation of this study is that we could not evaluate SARS-CoV-2 positivity.

CONCLUSIONS

Using an automated system of influenza-negative ILI outlier monitoring may have informed countries of the spread of COVID-19 more than 13 weeks before the first reported COVID-19 peaks. This proof-of-concept paper suggests that a system of influenza-negative ILI outlier monitoring could have informed national and global responses to SARS-CoV-2 during the rapid spread of this novel pathogen in early 2020.

Effectiveness of an mRNA vaccine booster dose against SARS-CoV-2 infection and severe COVID-19 in persons aged ≥60 years and other high-risk groups during predominant circulation of the delta variant in Italy, 19 July to 12 December 2021

BACKGROUND

Consolidated information on the effectiveness of COVID-19 booster vaccination in Europe are scarce.

RESEARCH DESIGN AND METHODS

We assessed the effectiveness of a booster dose of an mRNA vaccine against any SARS-CoV-2 infection (symptomatic or asymptomatic) and severe COVID-19 (hospitalization or death) after over two months from administration among priority target groups (n = 18,524,568) during predominant circulation of the Delta variant in Italy (July-December 2021).

RESULTS

Vaccine effectiveness (VE) against SARS-CoV-2 infection and, to a lesser extent, against severe COVID-19, among people ≥60 years and other high-risk groups (i.e. healthcare workers, residents in long-term-care facilities, and persons with comorbidities or immunocompromised), peaked in the time-interval 3-13 weeks (VE against infection = 67.2%, 95% confidence interval (CI): 62.5-71.3; VE against severe disease = 89.5%, 95% CI: 86.1-92.0) and then declined, waning 26 weeks after full primary vaccination (VE against infection = 12.2%, 95% CI: -4.7-26.4; VE against severe disease = 65.3%, 95% CI: 50.3-75.8). After 3-10 weeks from the administration of a booster dose, VE against infection and severe disease increased to 76.1% (95% CI: 70.4-80.7) and 93.0% (95% CI: 90.2-95.0), respectively.

CONCLUSIONS

These results support the ongoing vaccination campaign in Italy, where the administration of a booster dose four months after completion of primary vaccination is recommended.

Mortality in SARS-CoV-2 Hospitalized Patients Treated with Remdesivir: A Nationwide, Registry-Based Study in Italy

Remdesivir is the first drug approved for treatment of COVID-19 but current evidence for recommending its use for the treatment of moderate-to-severe disease is still controversial among clinical guidelines. We performed a nationwide, registry-based study including all Italian hospitalized patients with COVID-19 treated with remdesivir to assess the impact of major confounders on crude 15-day and 29-day mortality. Mortality was calculated using the Kaplan–Meier estimator and the Cox proportional-hazards model was applied to analyze the risks by patient’s baseline features. In total, 16,462 patients treated with remdesivir from 29 October 2020 to 17 December 2020 were entered in the study. Crude 15-day and 29-day mortality were 7.1% (95% CI, 6.7–7.5%) and 11.7% (95% CI, 11.2–12.2%), respectively. Being treated within two days of admission reduced the risk of death by about 40% (HR 1.4, 95% CI, 1.2–1.6). Results from the largest cohort of remdesivir-treated patients suggests that mortality in SARS-CoV-2 hospitalized patients is substantially influenced by the days between SARS-CoV-2 diagnosis and drug prescription. Current recommendations and future clinical trials for remdesivir alone or in combination should carefully consider the target population and timing for best efficacy of treatment.

Risk and protective factors for SARS-CoV-2 reinfections, surveillance data, Italy, August 2021 to March 2022

We explored the risk factors associated with SARS-CoV-2 reinfections in Italy between August 2021 and March 2022. Regardless of the prevalent virus variant, being unvaccinated was the most relevant risk factor for reinfection. The risk of reinfection increased almost 18-fold following emergence of the Omicron variant compared with Delta. A severe first SARS-CoV-2 infection and age over 60 years were significant risk factors for severe reinfection.

Immunity acquired by a minority active fraction of the population could explain COVID-19 spread in Greater Buenos Aires (June-November 2020)

The coronavirus disease 2019 (COVID-19) pandemic had an uneven development in different countries. In Argentina, the pandemic began in March 2020 and, during the first 3 months, the vast majority of cases were concentrated in a densely populated region that includes the city of Buenos Aires (country capital) and the Greater Buenos Aires (GBA) area that surrounds it. This work focuses on the spread of COVID-19 between June and November 2020 in GBA. Within this period of time there was no vaccine, basically only the early wild strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was present, and the official restriction and distancing measures in this region remained more or less constant. Under these particular conditions, the incidences show a sharp rise from June 2020 and begin to decrease towards the end of August until the end of November 2020. In this work we study, through mathematical modelling and available epidemiological information, the spread of COVID-19 in this region and period of time. We show that a coherent explanation of the evolution of incidences can be obtained assuming that only a minority fraction of the population got involved in the spread process, so that the incidences decreased as this group of people was becoming immune. The observed evolution of the incidences could then be a consequence at the population level of lasting immunity conferred by SARS-CoV-2.

Trends in outcomes following COVID-19 symptom onset in Milan: a cohort study

BACKGROUND

For people with symptomatic COVID-19, the relative risks of hospital admission, death without hospital admission and recovery without admission, and the times to those events, are not well understood. We describe how these quantities varied with individual characteristics, and through the first wave of the pandemic, in Milan, Italy.

METHODS

A cohort study of 27 598 people with known COVID-19 symptom onset date in Milan, Italy, testing positive between February and June 2020 and followed up until 17 July 2020. The probabilities of different events, and the times to events, were estimated using a mixture multistate model.

RESULTS

The risk of death without hospital admission was higher in March and April (for non-care home residents, 6%-8% compared with 2%-3% in other months) and substantially higher for care home residents (22%-29% in March). For all groups, the probabilities of hospitalisation decreased from February to June. The probabilities of hospitalisation also increased with age, and were higher for men, substantially lower for healthcare workers and care home residents, and higher for people with comorbidities. Times to hospitalisation and confirmed recovery also decreased throughout the first wave. Combining these results with our previously developed model for events following hospitalisation, the overall symptomatic case fatality risk was 15.8% (15.4%-16.2%).

CONCLUSIONS

The highest risks of death before hospital admission coincided with periods of severe burden on the healthcare system in Lombardy. Outcomes for care home residents were particularly poor. Outcomes improved as the first wave waned, community healthcare resources were reinforced and testing became more widely available.

Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19

With vaccination against COVID-19 stalled in some countries, increasing vaccine accessibility and distribution could help keep transmission under control. Here, we study the impact of reactive vaccination targeting schools and workplaces where cases are detected, with an agent-based model accounting for COVID-19 natural history, vaccine characteristics, demographics, behavioural changes and social distancing. In most scenarios, reactive vaccination leads to a higher reduction in cases compared with non-reactive strategies using the same number of doses. The reactive strategy could however be less effective than a moderate/high pace mass vaccination program if initial vaccination coverage is high or disease incidence is low, because few people would be vaccinated around each case. In case of flare-ups, reactive vaccination could better mitigate spread if it is implemented quickly, is supported by enhanced test-trace-isolate and triggers an increased vaccine uptake. These results provide key information to plan an adaptive vaccination rollout.

The authors use an agent-based model to investigate the potential of reactive vaccination strategies for COVID-19 outbreak mitigation. They find that distributing vaccines in schools and workplaces where cases are detected is more impactful than non-reactive strategies in a wide range of epidemic scenarios.

Fighting COVID in Central Asia: Governance Quality and Non-Pharmaceutical Effectiveness in the 'stans

To fight coronavirus disease 2019, non-pharmaceutical interventions were adopted all over the world. Non-pharmaceutical intervention (NPI) effectiveness also depends on governments’ capacity to implement sound policies. Stay-at-home orders are binding measures that can raise serious concerns among the population. The perceived quality and effective need for these measures are therefore crucial for the willingness of the citizens to accept NPIs. This study investigates the relationship between the efficacy of NPIs and governance quality in Central Asia. Results suggest that overall governance quality matters and that in this relationship regulatory quality is more important than rule of law, which matters more than government effectiveness.

The Effects of COVID-19 First Waves in Italy: An Answer Through a Retrospective Analysis of Mortality

Background

Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy.

Objective

This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series.

Methods

We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis.

Results

Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t₂₁=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals’ using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006).

Conclusions

Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19–related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.

Waiting for the truth: is reluctance in accepting an early origin hypothesis for SARS-CoV-2 delaying our understanding of viral emergence?

Two years after the start of the COVID-19 pandemic, key questions about the emergence of its aetiological agent (SARS-CoV-2) remain a matter of considerable debate. Identifying when SARS-CoV-2 began spreading among people is one of those questions. Although the current canonically accepted timeline hypothesises viral emergence in Wuhan, China, in November or December 2019, a growing body of diverse studies provides evidence that the virus may have been spreading worldwide weeks, or even months, prior to that time. However, the hypothesis of earlier SARS-CoV-2 circulation is often dismissed with prejudicial scepticism and experimental studies pointing to early origins are frequently and speculatively attributed to false-positive tests. In this paper, we critically review current evidence that SARS-CoV-2 had been circulating prior to December of 2019, and emphasise how, despite some scientific limitations, this hypothesis should no longer be ignored and considered sufficient to warrant further larger-scale studies to determine its veracity.

Inference on the dynamics of COVID-19 in the United States

The evolution of the COVID-19 pandemic is described through a time-dependent stochastic dynamic model in discrete time. The proposed multi-compartment model is expressed through a system of difference equations. Information on the social distancing measures and diagnostic testing rates are incorporated to characterize the dynamics of the various compartments of the model. In contrast with conventional epidemiological models, the proposed model involves interpretable temporally static and dynamic epidemiological rate parameters. A model fitting strategy built upon nonparametric smoothing is employed for estimating the time-varying parameters, while profiling over the time-independent parameters. Confidence bands of the parameters are obtained through a residual bootstrap procedure. A key feature of the methodology is its ability to estimate latent unobservable compartments such as the number of asymptomatic but infected individuals who are known to be the key vectors of COVID-19 spread. The nature of the disease dynamics is further quantified by relevant epidemiological markers that make use of the estimates of latent compartments. The methodology is applied to understand the true extent and dynamics of the pandemic in various states within the United States (US).

Co-circulation of SARS-CoV-2 Alpha and Gamma variants in Italy, February and March 2021

BackgroundSeveral SARS-CoV-2 variants of concern (VOC) have emerged through 2020 and 2021. There is need for tools to estimate the relative transmissibility of emerging variants of SARS-CoV-2 with respect to circulating strains.AimWe aimed to assess the prevalence of co-circulating VOC in Italy and estimate their relative transmissibility.MethodsWe conducted two genomic surveillance surveys on 18 February and 18 March 2021 across the whole Italian territory covering 3,243 clinical samples and developed a mathematical model that describes the dynamics of co-circulating strains.ResultsThe Alpha variant was already dominant on 18 February in a majority of regions/autonomous provinces (national prevalence: 54%) and almost completely replaced historical lineages by 18 March (dominant across Italy, national prevalence: 86%). We found a substantial proportion of the Gamma variant on 18 February, almost exclusively in central Italy (prevalence: 19%), which remained similar on 18 March. Nationally, the mean relative transmissibility of Alpha ranged at 1.55-1.57 times the level of historical lineages (95% CrI: 1.45-1.66). The relative transmissibility of Gamma varied according to the assumed degree of cross-protection from infection with other lineages and ranged from 1.12 (95% CrI: 1.03-1.23) with complete immune evasion to 1.39 (95% CrI: 1.26-1.56) for complete cross-protection.ConclusionWe assessed the relative advantage of competing viral strains, using a mathematical model assuming different degrees of cross-protection. We found substantial co-circulation of Alpha and Gamma in Italy. Gamma was not able to outcompete Alpha, probably because of its lower transmissibility.

COVID-19 response: effectiveness of weekly rapid risk assessments, Italy

Problem

After Italy’s first national restriction measures in 2020, a robust approach was needed to monitor the emerging epidemic of coronavirus disease 2019 (COVID-19) at subnational level and provide data to inform the strengthening or easing of epidemic control measures.

Approach

We adapted the European Centre for Disease Prevention and Control rapid risk assessment tool by including quantitative and qualitative indicators from existing national surveillance systems. We defined COVID-19 risk as a combination of the probability of uncontrolled transmission of severe acute respiratory syndrome coronavirus 2 and of an unsustainable impact of COVID-19 cases on hospital services, adjusted in relation to the health system’s resilience. The monitoring system was implemented with no additional cost in May 2020.

Local setting

The infectious diseases surveillance system in Italy uses consistent data collection methods across the country’s decentralized regions and autonomous provinces.

Relevant changes

Weekly risk assessments using this approach were sustainable in monitoring the epidemic at regional level from 4 May 2020 to 24 September 2021. The tool provided reliable assessments of when and where a rapid increase in demand for health-care services would occur if control or mitigation measures were not increased in the following 3 weeks.

Lessons learnt

Although the system worked well, framing the risk assessment tool in a legal decree hampered its flexibility, as indicators could not be changed without changing the law. The relative complexity of the tool, the impossibility of real-time validation and its use for the definition of restrictions posed communication challenges.