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Do variations in anomalous mortality in Europe in fall and winter of 2019–2020 tell us anything on the timing of SARS‐CoV‐2 outbreak?
Abstract
The present study makes a part of the already ample discussion on the subject of identification of the beginnings of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) coronavirus pandemics in the world and considers the following question: do the anomalies in death rates in the earlier periods bring any new knowledge of the subject? With the ultimate purpose of answering this question, spatial differences are analysed of excess mortality for the first time at such a detailed spatial scale in Europe. As it is known, according to current knowledge, a strong increase in coronavirus disease‐2019 mortality occurred in Lombardy, Italy, from about mid‐March (Week 11–12 of 2020), followed by Spain and Belgium. It was decided, in the context of the present study, to see if similarly strong mortality anomalies, not assigned to this factor, were not present earlier. This could constitute circumstantial evidence that SARS‐CoV‐2 coronavirus was present in Europe on a much larger scale earlier than it is commonly believed. The study, therefore, looks at whether there were local outbreaks of elevated deaths between November 2019 and March 2020. The analysis used available Eurostat data for 34 European countries according to the NUTS1, NUTS2 and NUTS3 divisions based on 918 units with more than 100,000 inhabitants. The number of deaths was analysed over consecutive 24 weeks of autumn–winter (7 October 2019–22 March 2020, i.e., W41‐2019 to W12‐2020) and were compared with the respective data for the 3‐year reference period 2016–2018. The method used identifies geographically concentrated areas with excess deaths over short periods relative to the reference period. It was shown that 44 regions between W41‐2019 and W08‐2020 (i.e., before February 23) had elevated mortality (115% or more relative to the reference period). In the 44 NUTS3 regions mentioned, excess deaths during the autumn–winter period amounted to 96,000–126,000 when compared with the 2016–2018 baseline period (mainly in Spain, France, Italy, United Kingdom). It cannot be excluded that to some extent this could have been due to SARS‐CoV‐2 coronavirus infections. To confirm or deny this more clearly, detailed studies of the recorded causes of death in the indicated 44 regions are needed.
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As a reference laboratory for measles and rubella surveillance in Lombardy, we evaluated the association between SARS-CoV-2 infection and measles-like syndromes, providing preliminary evidence for undetected early circulation of SARS-CoV-2. Overall, 435 samples from 156 cases were investigated. RNA from oropharyngeal swabs (N = 148) and urine (N = 141) was screened with four hemi-nested PCRs and molecular evidence for SARS-CoV-2 infection was found in 13 subjects. Two of the positive patients were from the pandemic period (2/12, 16.7%, March 2020–March 2021) and 11 were from the pre-pandemic period (11/44, 25%, August 2019–February 2020). Sera (N = 146) were tested for anti-SARS-CoV-2 IgG, IgM, and IgA antibodies. Five of the RNA-positive individuals also had detectable anti-SARS-CoV-2 antibodies. No strong evidence of infection was found in samples collected between August 2018 and July 2019 from 100 patients. The earliest sample with evidence of SARS-CoV-2 RNA was from September 12, 2019, and the positive patient was also positive for anti-SARS-CoV-2 antibodies (IgG and IgM). Mutations typical of B.1 strains previously reported to have emerged in January 2020 (C3037T, C14408T, and A23403G), were identified in samples collected as early as October 2019 in Lombardy. One of these mutations (C14408T) was also identified among sequences downloaded from public databases that were obtained by others from samples collected in Brazil in November 2019. We conclude that a SARS-CoV-2 progenitor capable of producing a measles-like syndrome may have emerged in late June-late July 2019 and that viruses with mutations characterizing B.1 strain may have been spreading globally before the first Wuhan outbreak. Our findings should be complemented by high-throughput sequencing to obtain additional sequence information. We highlight the importance of retrospective surveillance studies in understanding the early dynamics of COVID-19 spread and we encourage other groups to perform retrospective investigations to seek confirmatory proofs of early SARS-CoV-2 circulation.
In March 2020, the first pandemic caused by a coronavirus was declared by the World Health Organization. Italy was one of the first and most severely affected countries, particularly the northern part of the country. The latest evidence suggests that the virus could have been circulating, at least in Italy, before the first autochthonous SARS-COV-2 case was detected in February 2020. The present study aimed to investigate the presence of antibodies against SARS-CoV-2 in human serum samples collected in the last months of 2019 (September–December) in the Apulia region, Southern Italy. Eight of 455 samples tested proved positive on in-house receptor-binding-domain-based ELISA. Given the month of collection of the positive samples, these findings may indicate early circulation of SARS-CoV-2 in Apulia region in the autumn of 2019. However, it cannot be completely ruled out that the observed sero-reactivity could be an unknown antigen specificity in another virus to which subjects were exposed containing an epitope adventitiously cross-reactive with an epitope of SARS-CoV-2.
Background:
The global emergence of coronavirus disease 2019 (COVID-19) has challenged healthcare and rapidly spread over the globe. Early detection of new infections is crucial in the control of emerging diseases. Evidence of early recorded COVID-19 cases outside China has been documented in various countries. In this study, we aimed to identify the time of SARS-CoV-2 infection circulation by retrospectively analyzing sera of measles patients, weeks before the reported first COVID-19 cases in Angola.
Materials and methods:
We examined the humoral response against SARS-CoV-2 by using an enzyme-linked immunosorbent assay (ELISA)-based assay on a combined two-step sandwich enzyme immunoassay method. In total, we received 568 study patients with blood specimens collected from 23 September 2019 to 28 February 2020, 442 sera samples that met the criteria of the study were withdrawn and selected from the overall 568 received samples. In this study, we considered seropositives, patients who tested positive for SARS-CoV-2 immunoglobulin G (IgG) and M (IgM) antibodies with the index value >1.
Results:
Of the 442 sera samples that met the criteria of the study, 204 were measles seropositive. Forty out of 204 were confirmed reactive to SARS-CoV-2 viral proteins using IgG and IgM more than 2 weeks before the first reported case in Angola. The humoral response analysis showed significant differences (p = 0.01) between the IgG and IgM indexes in the unvaccinated measles patients. Similarly, a significant difference (p = 0.001) was seen between the IgG and IgM indexes in the vaccinated measles patients.
Conclusion:
Here, using the humoral response analysis, we report the identification of early circulation SARS-CoV-2 infection weeks before the first recognized cases in the Republic of Angola.
We studied severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence among pregnant women in Norway by including all women who were first trimester pregnant (n = 6520), each month from December 2019 through December 2020, in the catchment region of Norway's second-largest hospital. We used sera that had been frozen stored after compulsory testing for syphilis antibodies in antenatal care. The sera were analysed with the Elecsys® Anti-SARS-CoV-2 immunoassay (Roche Diagnostics, Cobas e801). This immunoassay detects IgG/IgM against SARS-CoV-2 nucleocapsid antigen. Sera with equivocal or positive test results were retested with the Liaison® SARS-CoV-2 S1/S2 IgG (DiaSorin), which detects IgG against the spike (S)1 and S2 protein on the SARS-CoV-2 virus. In total, 98 women (adjusted prevalence 1.7%) had SARS CoV-2 antibodies. The adjusted seroprevalence increased from 0.3% (1/445) in December 2019 to 5.7% (21/418) in December 2020. Out of the 98 seropositive women, 36 (36.7%) had serological signs of current SARS-CoV-2 infection at the time of serum sampling, and the incidence remained low during the study period. This study suggests that SARS CoV-2 was present in the first half of December 2019, 6 weeks before the first case was recognised in Norway. The low occurrence of SARS-CoV-2 infection during 2020, may be explained by high compliance to extensive preventive measures implemented early in the epidemic.
The massive emergence of COVID-19 cases in the first phase of pandemic within an
extremely short period of time suggest that an undetected earlier circulation of SARS-CoV-2 might
have occurred. Given the importance of this evidence, an independent evaluation was recommended
by the World Health Organization (WHO) to test a subset of samples selected on the level of positivity
in ELISA assays (positive, low positive, negative) detected in our previous study of prepandemic
samples collected in Italy. SARS-CoV-2 antibodies were blindly retested by two independent centers
in 29 blood samples collected in the prepandemic period in Italy, 29 samples collected one year
before and 11 COVID-19 control samples. The methodologies used included IgG-RBD/IgM-RBD
ELISA assays, a qualitative micro-neutralization CPE-based assay, a multiplex IgG protein array, an
ELISA IgM kit (Wantai), and a plaque-reduction neutralization test. The results suggest the presence
of SARS-CoV-2 antibodies in some samples collected in the prepandemic period, with the oldest
samples found to be positive for IgM by both laboratories collected on 10 October 2019 (Lombardy),
11 November 2019 (Lombardy) and 5 February 2020 (Lazio), the latter with neutralizing antibodies.
The detection of IgM and/or IgG binding and neutralizing antibodies was strongly dependent on the
different serological assays and thresholds employed, and they were not detected in control samples
collected one year before. These findings, although gathered in a small and selected set of samples,
highlight the importance of harmonizing serological assays for testing the spread of the SARS-CoV-2
virus and may contribute to a better understanding of future virus dynamics.
Background
In the night of February 20, 2020, the first epidemic of the novel coronavirus disease (COVID-19) outside Asia was uncovered by the identification of its first patient in Lombardy region, Italy. In the following weeks, Lombardy experienced a sudden increase in the number of ascertained infections and strict measures were imposed to contain the epidemic spread.
Methods
We analyzed official records of cases occurred in Lombardy to characterize the epidemiology of SARS-CoV-2 during the early phase of the outbreak. A line list of laboratory-confirmed cases was set up and later retrospectively consolidated, using standardized interviews to ascertained cases and their close contacts. We provide estimates of the serial interval, of the basic reproduction number, and of the temporal variation of the net reproduction number of SARS-CoV-2.
Results
Epidemiological investigations detected over 500 cases (median age: 69, IQR: 57–78) before the first COVID-19 diagnosed patient (February 20, 2020), and suggested that SARS-CoV-2 was already circulating in at least 222 out of 1506 (14.7%) municipalities with sustained transmission across all the Lombardy provinces. We estimated the mean serial interval to be 6.6 days (95% CrI, 0.7–19). Our estimates of the basic reproduction number range from 2.6 in Pavia (95% CI, 2.1–3.2) to 3.3 in Milan (95% CI, 2.9–3.8). A decreasing trend in the net reproduction number was observed following the detection of the first case.
Conclusions
At the time of first case notification, COVID-19 was already widespread in the entire Lombardy region. This may explain the large number of critical cases experienced by this region in a very short timeframe. The slight decrease of the reproduction number observed in the early days after February 20, 2020 might be due to increased population awareness and early interventions implemented before the regional lockdown imposed on March 8, 2020.
Since 11 March 2020 when officially declared a global pandemic, Covid‐19 (or SARS‐COV2) has turned out to be a multifaceted disease differently affecting countries and individuals. What makes certain countries more vulnerable than others has attracted the interest of scientists from various disciplines. This paper intends to compare the impact of demographic parameters, population health conditions and policy actions on prevalence and fatality levels of Covid‐19 during the first 3 months since its declaration of global pandemic. A country‐level exploratory analysis has been conducted in order to assess how demography, national health conditions and measures taken interact and condition the disease outcomes. Analysis relies on publicly available data on Covid‐19 reported cases, deaths and number of persons tested. Those data are combined with demographic parameters (sex ratio, mean age, population density and life expectancy), health data (cardiovascular death rate, diabetes prevalence, share of smokers among males and females and number of hospital beds) and information about relative national policies aiming the management of the pandemic (lockdown timing and duration). Our analysis confirms the diversity of factors and the complexity of their interaction in explaining the propagation and fatality of the disease across Europe. Our findings question some well‐established attitudes concerning the role of demographic variables and public health conditions in the spread of the disease.
Health inequalities—systematic differences in health outcomes between social groups and across spatial units—are ubiquitous, but not necessarily inevitable. They are the product of a complex interplay of social and economic processes operating at various scales. The unequal pattern of infection and death seen in the Covid‐19 pandemic has served to highlight the stark social gradient in health that exists within many European countries. Although the complex social determinants of health have been studied for many decades, there is still a great deal of work to do to elucidate explanations for health inequalities across time and space. To rise to the challenge, we need high‐quality, representative data capable of capturing multi‐scalar longitudinal processes. This special issue brings together eight new studies which all use national population register data linked with various other sources of administrative data (e.g., residence, tax and health records) to investigate different vectors of inequalities in health and mortality, covering spatial, socioeconomic, ethnic and migrant status. This editorial outlines their contributions, argues for the invaluable role of population register data to understand health inequalities and suggests promising future research avenues.
Questions persist as to the origin of the COVID-19 pandemic. Evidence is building that its origin as a zoonotic spillover occurred prior to the officially accepted timing of early December, 2019. Here we provide novel methods to date the origin of COVID-19 cases. We show that six countries had exceptionally early cases, unlikely to represent part of their main case series. The model suggests a likely timing of the first case of COVID-19 in China as November 17 (95% CI October 4). Origination dates are discussed for the first five countries outside China and each continent. Results infer that SARS-CoV-2 emerged in China in early October to mid-November, and by January, had spread globally. This suggests an earlier and more rapid timeline of spread. Our study provides new approaches for estimating dates of the arrival of infectious diseases based on small samples that can be applied to many epidemiological situations.
The first cluster of coronavirus cases in Europe was officially detected on 21st February 2020 in Northern Italy, even if recent evidence showed sporadic first cases in Europe since the end of 2019. In this study, we have tested the presence of coronavirus in Italy and, even more importantly, we have assessed whether the virus had already spread sooner than 21st February. We use a counterfactual approach and certified daily data on the number of deaths (deaths from any cause, not only related to coronavirus) at the municipality level. Our estimates confirm that coronavirus began spreading in Northern Italy in mid-January.
Often presented as a global pandemic spreading all over the world, the Covid-19 disease, however, hit not only countries but also regions differently. The objective of this paper is to focus on the spatial heterogeneity and to contribute to the understanding of the channels of spread of the Covid-19 epidemic focusing on the regional socio-economic dimension. We use, for that, an original dataset covering 125 European regions in 12 countries. Considering that the impact of the COVID-19 crisis, sharply differed not only across countries, but also across regions within the same countries, the empirical strategy is based, on the one hand, on an explanatory analysis of spatial autocorrelation which makes it possible to identify the regional clusters of the disease. On the other hand, we use spatial regression models to capture the diffusion effect and the role of different families of territorial factors in this process. We find that the rate of older people, GDP per capita, unemployment rate, and poverty positively impact the mortality rate related to Covid-19. In contrast, medical practitioners, hospital beds and finally, social trust have a negative incidence on the death rate.
Mortality due to massive events like the COVID-19 pandemic is underestimated because of several reasons, among which the impossibility to track all positive cases and the inadequacy of coding systems are presumably the most relevant. Therefore, the most affordable method to estimate COVID-19-related mortality is excess mortality (EM). Very often, though, EM is calculated on large spatial units that may entail different EM patterns and without stratifying deaths by age or sex, while, especially in the case of epidemics, it is important to identify the areas that suffered a higher death toll or that were spared. We developed the Stata COVID19_EM.ado procedure that estimates EM within municipalities in six subgroups defined by sex and age class using official data provided by ISTAT (Italian National Statistics Bureau) on deaths occurred from 2015 to 2020. Using simple linear regression models, we estimated the mortality trend in each age-and-sex subgroup and obtained the expected deaths of 2020 by extrapolating the linear trend. The results are then displayed using choropleth maps. Subsequently, local autocorrelation maps, which allow to appreciate the presence of local clusters of high or low EM, may be obtained using an R procedure that we developed.
•We focused on estimating excess mortality in small-scale spatial units (municipalities) and in population strata defined by age and sex.
•This method gives a deeper insight on excess mortality than summary figures at regional or national level, enabling to identify the local areas that suffered the most and the high-risk population subgroups within them.
•This type of analysis could be replicated on different time frames, which might correspond to successive epidemic waves, as well as to periods in which containment measures were enforced and for different age classes; moreover, it could be applied in every instance of mortality crisis within a region or a country.
Two large wastewater treatment plants (WWTP), covering around 2.7 million inhabitants, which represents around 85% of the metropolitan area of Barcelona, were sampled before, during, and after the implementation of a complete lockdown. Five one-step reverse transcriptase quantitative PCR (RT-qPCR) assays, targeting the poly-merase(IP2 andIP4), theenvelope(E), andthenucleoprotein (N1 andN2) genome regions, were employed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detection in 24-h composite wastewater samples concentrated by polyethylene glycol (PEG) precipitation. SARS-CoV-2 was detected in a sewage sample collected 41 days ahead of the declaration of the first COVID-19 case. The evolution of SARS-CoV-2 genome copies in wastewater evidenced the validity of water-based epidemiology (WBE) to anticipate COVID-19 outbreaks, to evaluate the impact of control measures, and even to estimate the burden of shedders, including presymptomatic, asymptomatic, symptomatic, and undiagnosed cases. For the latter objective, a model was applied for the estimation of the total number of shedders, evidencing a high proportion of asymptomatic infected individuals. In this way, an infection prevalence of 2.0 to 6.5% was figured. On the other hand, proportions of around 0.12% and 0.09% of the total population were determined to be required for positive detection in the two WWTPs. At the end of the lockdown, SARS-CoV-2 RNA apparently disappeared in the WWTPs but could still be detected in grab samples from four urban sewers. Sewer monitoring allowed for location of specific hot spots of COVID-19, enabling the rapid adoption of appropriate mitigation measures. © 2021. American Society for Microbiology. All Rights Reserved.
We analyze data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2019–2020, before the first public announcements of local sources of infection were made. We show evidence that unexpected levels of concerns about cases of pneumonia were raised across a number of European countries. Whistleblowing came primarily from the geographical regions that eventually turned out to be the key breeding grounds for infections. These findings point to the urgency of setting up an integrated digital surveillance system in which social media can help geo-localize chains of contagion that would otherwise proliferate almost completely undetected.
Lombardy, the most populated and industrialized Italian region, was the epicentre of the first wave (March and April, 2020) of COVID-19 in Italy and it is among the most air polluted areas of Europe. We carried out an ecological study to assess the association between long-term exposure to particulate matter (PM) and nitrogen dioxide (NO2) on COVID-19 incidence and all-cause mortality after accounting for demographic, socioeconomic and meteorological variables.
The study was based on publicly available data. Multivariable negative binomial mixed regression models were fitted, and results were reported in terms of incidence rate ratios (IRRs) and standardized mortality ratios (SMR). The effect of winter temperature and humidity was modelled through restricted cubic spline.
Data from 1,439 municipalities out of 1,507 (95%) were included in the analyses, leading to a total of 61,377 COVID-19 cases and 40,401 deaths from all-causes collected from February 20th to April 16th and from March 1st to April 30th, 2020, respectively. Several demographic and socioeconomic variables resulted significantly associated with COVID-19 incidence and all-cause mortality in a multivariable fashion. An increase in average winter temperature was associated with a nonlinear decrease in COVID-19 incidence and all-cause mortality, while an opposite trend emerged for the absolute humidity. An increase of 10 μg/m³ in the mean annual concentrations of PM2.5 and PM10 over the previous years was associated with a 58% and 34% increase in COVID-19 incidence rate, respectively. Similarly, a 10 μg/m³ increase of annual mean PM2.5 concentration was associated with a 23% increase in all-cause mortality. An inverse association was found between NO2 levels and COVID-19 incidence and all-cause mortality.
Our ecological study showed that exposure to PM was significantly associated with the COVID-19 incidence and excess mortality during the first wave of the outbreak in Lombardy, Italy.
We identified severe acute respiratory syndrome coronavirus 2 RNA in an oropharyngeal swab specimen collected from a child with suspected measles in early December 2019, ≈3 months before the first identified coronavirus disease case in Italy. This finding expands our knowledge on timing and mapping of novel coronavirus transmission pathways.
Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 mortality in England using high geographical resolution. In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level (n = 32,844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014–2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We find a 0.5% (95% credible interval: −0.2%, 1.2%) and 1.4% (95% CrI: −2.1%, 5.1%) increase in COVID-19 mortality risk for every 1 μg/m³ increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain.
Background
The high contagiousness and rapid spreading of the coronavirus disease 2019 (COVID-19) has caused a high number of critical to severe life-threatening cases, which required urgent hospital admission and treatment in intensive care units (ICUs). The pandemic has been a tough test for all European national health systems and their capability to provide an adequate reaction.
Methods
The present work aims to reveal correlations between parameters such as COVID-19 incidence, ICU bed occupancy, ICU excess area, and mortality in Italian regions. Public data for the period of March 1 to July 16, 2020, were analyzed using several mathematical and statistical methods.
Results
The analysis defined two separate groups of Italian regions. The examined variables considered within these groups were interlinked and dependent on each other. The regions of the two groups shared the same kind of fitted model (linear) explaining mortality as a function of cumulative incidence, but with higher value of the constant in one group, so characterized by a high intrinsic “strength” of the pandemic, certainly playing a major role in the generation of a large number of severe and life-threatening cases. These results are confirmed at European level. Other factors may condition mortality and be linked to incidence, such as ICU saturation and excess.
Conclusions
These quantitative results could be a very helpful tool to set up preventive measures and optimize biomedical interventions before the pandemic, in its recurrent waves, could overcome the reaction capacity of any public health system.
When the population risk factors and reporting systems are similar, the assessment of the case-fatality (or lethality) rate (ratio of cases to deaths) represents a perfect tool for analyzing, understanding and improving the overall efficiency of the health system. The objective of this article is to estimate the influence of the hospital care system on lethality in metropolitan France during the inception of the COVID-19 epidemic, by analyzing the spatial variability of the hospital case-fatality rate (CFR) between French districts. In theory, the hospital age-standardized CFR should not display significant differences between districts, since hospital lethality depends on the virulence of the pathogen (the SARS-CoV-2 virus), the vulnerability of the population (mainly age-related), the healthcare system quality, and cases and deaths definition and the recording accuracy. We analyzed hospital data on COVID-19 hospitalizations, severity (admission to intensive care units for reanimation or endotracheal intubation) and mortality, from March 19 to May 8 corresponding to the first French lockdown. All rates were age-standardized to eliminate differences in districts age structure. The results show that the higher case-fatality rates observed by districts are mostly related to the level of morbidity. Time analysis shows that the case-fatality rate has decreased over time, globally and in almost all districts, showing an improvement in the management of severe patients during the epidemic. In conclusion, it appears that during the first critical phase of COVID-19 ramping epidemic in metropolitan France, the higher case-fatality rates were generally related to the higher level of hospitalization, then potentially related to the overload of healthcare system. Also, low hospitalization with high case-fatality rates were mostly found in districts with low population density, and could due to some limitation of the local healthcare access. However, the magnitude of this increase of case-fatality rate represents less than 10 per cent of the average case-fatality rate, and this variation is small compared to much greater variation across countries reported in the literature.
The aim of this article is to analyse the sub‐national spread of COVID‐19 in Italy using an economic geography perspective. The striking spatial unevenness of COVID‐19 suggests that the infection has hit economic core locations harder, and this raises questions about whether, and how, the sub‐national geography of the disease is connected to the economic base of localities. We provide some first evidence consistent with the possibility that the local specialisation in geographically concentrated economic activities acts as a vehicle of disease transmission. This could generate a core‐periphery pattern in the spatiality of COVID‐19, which might follow the lines of the local economic landscape and the tradability of its outputs.
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Within two weeks from the first detection of the SARS‐CoV‐2 positive patient on 21st February, from Lombardy the disease has spread over every region in Italy. The main objective of this study is to identify spatial effects and spatiotemporal patterns of the outbreak of COVID‐19 in different regions of Italy. Spatial indicators for different periods, as Moran’s I, local Moran, LISA clusters, Getis and Ord G, and scatterplots are used for this purpose. Results confirm the great presence of spatial effects as well as changes in spatial regimes between the quarantine and the easing phase. The evidence could be of help for policymakers to a proper assessments of health strategies aware of local characteristics.
There are no robust data on the real onset of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and spread in the prepandemic period worldwide. We investigated the presence of SARS-CoV-2 receptor-binding domain (RBD)–specific antibodies in blood samples of 959 asymptomatic individuals enrolled in a prospective lung cancer screening trial between September 2019 and March 2020 to track the date of onset, frequency, and temporal and geographic variations across the Italian regions. SARS-CoV-2 RBD-specific antibodies were detected in 111 of 959 (11.6%) individuals, starting from September 2019 (14%), with a cluster of positive cases (>30%) in the second week of February 2020 and the highest number (53.2%) in Lombardy. This study shows an unexpected very early circulation of SARS-CoV-2 among asymptomatic individuals in Italy several months before the first patient was identified, and clarifies the onset and spread of the coronavirus disease 2019 (COVID-19) pandemic. Finding SARS-CoV-2 antibodies in asymptomatic people before the COVID-19 outbreak in Italy may reshape the history of pandemic.
The effects of and the response to COVID19 are uneven both globally and locally. This paper adopts a regional perspective considering the link between life expectancy – as a proxy for the quality of the national health system - and the distribution of both tests for COVID19 and death rates across Italian regions. Expectedly, a positive link between life expectancy and diagnostic effort is detected. However, a rather counterintuitive positive link is found also between the death rate and life expectancy. Nonetheless, the latter is broadly not statistically significant. These results while calling for further research, cast doubts about the main drivers of the observed regional patterns.
A second wave pandemic constitutes an imminent threat to society, with a potentially immense toll in terms of human lives and a devastating economic impact. We employ the epidemic Renormalisation Group (eRG) approach to pandemics, together with the first wave data for COVID-19, to efficiently simulate the dynamics of disease transmission and spreading across different European countries. The framework allows us to model, not only inter and extra European border control effects, but also the impact of social distancing for each country. We perform statistical analyses averaging on different level of human interaction across Europe and with the rest of the World. Our results are neatly summarised as an animation reporting the time evolution of the first and second waves of the European COVID-19 pandemic. Our temporal playbook of the second wave pandemic can be used by governments, financial markets, the industries and individual citizens, to efficiently time, prepare and implement local and global measures.
The novel coronavirus Covid‐19 was brought to the global spotlight in early 2020 and has already had significant impacts on daily life, while the effects could last for a long period. However, these impacts appear to have been regionally differentiated, since similar to previous pandemics, geography plays an important role in viruses’ diffusion. This paper enriches our knowledge about the initial territorial impact of the pandemic, from January to May 2020, studying the spread of Covid‐19 across 119 regional economies in 9 EU countries and explaining its underlying factors. Air quality, demographics, global interconnectedness, urbanization trends, historic trends in health expenditure as well as the policies implemented to mitigate the pandemic were found to have influenced the regionally uneven mortality rate of Covid‐19.
Through a weekly all-cause mortality surveillance system, we observed in France a major all-cause excess mortality from March to May 2020, concomitant with the coronavirus disease (COVID-19) epidemic. The excess mortality was 25,030 deaths, mainly among elderly people. Five metropolitan regions were the most affected, particularly Île-de-France and the Grand-Est regions. Assessing the excess mortality related to COVID-19 is complex because of the potential protective effect of the lockdown period on other causes of mortality.
The pressing need to restart socioeconomic activities locked-down to control the spread of SARS-CoV-2 in Italy must be coupled with effective methodologies to selectively relax containment measures. Here we employ a spatially explicit model, properly attentive to the role of inapparent infections, capable of: estimating the expected unfolding of the outbreak under continuous lockdown (baseline trajectory); assessing deviations from the baseline, should lockdown relaxations result in increased disease transmission; calculating the isolation effort required to prevent a resurgence of the outbreak. A 40% increase in effective transmission would yield a rebound of infections. A control effort capable of isolating daily ~5.5% of the exposed and highly infectious individuals proves necessary to maintain the epidemic curve onto the decreasing baseline trajectory. We finally provide an ex-post assessment based on the epidemiological data that became available after the initial analysis and estimate the actual disease transmission that occurred after weakening the lockdown.
Background:
Standardized mortality surveillance data, capable of detecting variations in total mortality at population level and not only among the infected, provide an unbiased insight into the impact of epidemics, like COVID-19 (Coronavirus disease). We analysed the temporal trend in total excess mortality and deaths among positive cases of SARS-CoV-2 by geographical area (north and centre-south), age and sex, taking into account the deficit in mortality in previous months.
Methods:
Data from the Italian rapid mortality surveillance system was used to quantify excess deaths during the epidemic, to estimate the mortality deficit during the previous months and to compare total excess mortality with deaths among positive cases of SARS-CoV-2. Data were stratified by geographical area (north vs centre and south), age and sex.
Results:
COVID-19 had a greater impact in northern Italian cities among subjects aged 75-84 and 85+ years. COVID-19 deaths accounted for half of total excess mortality in both areas, with differences by age: almost all excess deaths were from COVID-19 among adults, while among the elderly only one third of the excess was coded as COVID-19. When taking into account the mortality deficit in the pre-pandemic period, different trends were observed by area: all excess mortality during COVID-19 was explained by deficit mortality in the centre and south, while only a 16% overlap was estimated in northern cities, with quotas decreasing by age, from 67% in the 15-64 years old to 1% only among subjects 85+ years old.
Conclusions:
An underestimation of COVID-19 deaths is particularly evident among the elderly. When quantifying the burden in mortality related to COVID-19, it is important to consider seasonal dynamics in mortality. Surveillance data provides an impartial indicator for monitoring the following phases of the epidemic, and may help in the evaluation of mitigation measures adopted.
Background
Since a national lockdown was introduced across the UK in March, 2020, in response to the COVID-19 pandemic, cancer screening has been suspended, routine diagnostic work deferred, and only urgent symptomatic cases prioritised for diagnostic intervention. In this study, we estimated the impact of delays in diagnosis on cancer survival outcomes in four major tumour types.
Methods
In this national population-based modelling study, we used linked English National Health Service (NHS) cancer registration and hospital administrative datasets for patients aged 15–84 years, diagnosed with breast, colorectal, and oesophageal cancer between Jan 1, 2010, and Dec 31, 2010, with follow-up data until Dec 31, 2014, and diagnosed with lung cancer between Jan 1, 2012, and Dec 31, 2012, with follow-up data until Dec 31, 2015. We use a routes-to-diagnosis framework to estimate the impact of diagnostic delays over a 12-month period from the commencement of physical distancing measures, on March 16, 2020, up to 1, 3, and 5 years after diagnosis. To model the subsequent impact of diagnostic delays on survival, we reallocated patients who were on screening and routine referral pathways to urgent and emergency pathways that are associated with more advanced stage of disease at diagnosis. We considered three reallocation scenarios representing the best to worst case scenarios and reflect actual changes in the diagnostic pathway being seen in the NHS, as of March 16, 2020, and estimated the impact on net survival at 1, 3, and 5 years after diagnosis to calculate the additional deaths that can be attributed to cancer, and the total years of life lost (YLLs) compared with pre-pandemic data.
Findings
We collected data for 32 583 patients with breast cancer, 24 975 with colorectal cancer, 6744 with oesophageal cancer, and 29 305 with lung cancer. Across the three different scenarios, compared with pre-pandemic figures, we estimate a 7·9–9·6% increase in the number of deaths due to breast cancer up to year 5 after diagnosis, corresponding to between 281 (95% CI 266–295) and 344 (329–358) additional deaths. For colorectal cancer, we estimate 1445 (1392–1591) to 1563 (1534–1592) additional deaths, a 15·3–16·6% increase; for lung cancer, 1235 (1220–1254) to 1372 (1343–1401) additional deaths, a 4·8–5·3% increase; and for oesophageal cancer, 330 (324–335) to 342 (336–348) additional deaths, 5·8–6·0% increase up to 5 years after diagnosis. For these four tumour types, these data correspond with 3291–3621 additional deaths across the scenarios within 5 years. The total additional YLLs across these cancers is estimated to be 59 204–63 229 years.
Interpretation
Substantial increases in the number of avoidable cancer deaths in England are to be expected as a result of diagnostic delays due to the COVID-19 pandemic in the UK. Urgent policy interventions are necessary, particularly the need to manage the backlog within routine diagnostic services to mitigate the expected impact of the COVID-19 pandemic on patients with cancer.
Funding
UK Research and Innovation Economic and Social Research Council.
The present work estimates the increased risk of coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 by establishing the linkage between the mortality rate in the infected cases and the air pollution, specifically Particulate Matters (PM) with aerodynamic diameters ≤ 10 µm and ≤ 2.5 µm. Data related to nine Asian cities are analyzed using statistical approaches, including the analysis of variance and regression model. The present work suggests that there exists a positive correlation between the level of air pollution of a region and the lethality related to COVID-19, indicating air pollution to be an elemental and concealed factor in aggravating the global burden of deaths related to COVID-19. Past exposures to high level of PM2.5 over a long period, is found to significantly correlate with present COVID-19 mortality per unit reported cases (p < 0.05) compared to PM10, with non-significant correlation (p = 0.118). The finding of the study can help government agencies, health ministries and policymakers globally to take proactive steps by promoting immunity-boosting supplements and appropriate masks to reduce the risks associated with COVID-19 in highly polluted areas.
Following SARS-CoV-2 emergence in China, a specific surveillance was implemented in France. Phylogenetic analysis of sequences retrieved through this surveillance suggests that detected initial introductions, involving non-clade G viruses, did not seed local transmission. Nevertheless, identification of clade G variants subsequently circulating in the country, with the earliest from a patient who neither travelled to risk areas nor had contact with travellers, suggests that SARS-CoV-2 might have been present before the first recorded local cases.
The COVID-19 epidemic appeared in Poland in early March 2020. Since then, the epidemic has impacted strongly on the socioeconomic nature of the country. Since April 2020, the region with the most confirmed cases is the province of Silesia (Śląskie). This paper has two main aims. First, the authors focus on explanations for the causes of the large number of confirmed cases in this region. The second issue is the mosaic pattern of COVID-19 cases in Śląskie province.
The paper explains these patterns with reference to urban shrinkage, trans-industrialism, hard coal mining and polycentricity. The impact of these drivers is clearly visible in the local patterning of the pandemic. The authors point out that this specific relationship in this province complicates regional and local policy and indirectly affects state policy focused on the epidemic as well. The key problem is the negative impact of COVID-19 on hard coal mining. This sector, in turn,
is considered by the Polish government as one of the most important from an economic and political point of view.
We evaluated SARS-CoV-2 RNA and neutralising antibodies in blood donors (BD) residing in the Lodi Red Zone, Italy. Of 390 BDs recruited after 20 February 2020 − when the first COVID-19 case in Lombardy was identified, 91 (23%) aged 19-70years were antibody positive. Viral RNA was detected in an additional 17 (4.3%) BDs, yielding ca 28% (108/390) with evidence of virus exposure. Five stored samples collected as early as 12 February were seropositive. © 2020 European Centre for Disease Prevention and Control (ECDC). All rights reserved.
Long-term exposure to air pollutant concentrations is known to cause chronic lung inflammation, a condition that may promote increased severity of COVID-19 syndrome caused by the novel coronavirus. In this paper, we empirically investigate the ecologic association between longterm exposure to fine particulate matter (PM) concentration and excess deaths in the first quarter of 2020 in municipalities of Northern Italian. The study accounts for potentially spatial confounding factors related to urbanization that may have influenced the spreading of the novel coronavirus. Our epidemiological analysis uses geographical information (e.g., municipalities) and Poisson regression to assess whether both ambient PM concentration and excess mortality have a similar spatial distribution. Preliminary evidence confirms the hypothesis and suggests a positive association of ambient PM on excess mortality in Northern Italy
More than three years have passed since the first case of a new coronavirus infection (SARS-CoV-2) in the city of Wuhan (Hubei, China). The Wuhan Institute of Virology was founded in that city in 1956 and the country's first biosafety level 4 laboratory opened within that center in 2015. The coincidence that the first cases of infection emerged in the city where the virology institute's headquarters is located, the failure to 100% identify the virus' RNA in any of the coronaviruses isolated in bats, and the lack of evidence on a possible intermediate animal host in the contagion's transmission make it so that at present, there are doubts about the real origin of SARS-CoV-2. This article will review two theories: SARS-CoV-2 as a virus of zoonotic origin or as a leak from the high-level biosafety laboratory in Wuhan.
Wastewater-based epidemiology (WBE) has drawn great attention since the Coronavirus disease 2019 (COVID-19) pandemic, not only due to its capability to circumvent the limitations of traditional clinical surveillance, but also due to its potential to forewarn fluctuations of disease incidences in communities. One critical application of WBE is to provide "early warnings" for upcoming fluctuations of disease incidences in communities which traditional clinical testing is incapable to achieve. While intricate models have been developed to determine early warnings based on wastewater surveillance data, there is an exigent need for straightforward, rapid, broadly applicable methods for health departments and partner agencies to implement. Our purpose in this study is to develop and evaluate such early-warning methods and clinical-case peak-detection methods based on WBE data to mount an informed public health response. Throughout an extended wastewater surveillance period across Detroit, MI metropolitan area (the entire study period is from September 2020 to May 2022) we designed eight early-warning methods (three real-time and five post-factum). Additionally, we designed three peak-detection methods based on clinical epidemiological data. We demonstrated the utility of these methods for providing early warnings for COVID-19 incidences, with their counterpart accuracies evaluated by hit rates. "Hit rates" were defined as the number of early warning dates (using wastewater surveillance data) that captured defined peaks (using clinical epidemiological data) divided by the total number of early warning dates. Hit rates demonstrated that the accuracy of both real-time and post-factum methods could reach 100 %. Furthermore, the results indicate that the accuracy was influenced by approaches to defining peaks of disease incidence. The proposed methods herein can assist health departments capitalizing on WBE data to assess trends and implement quick public health responses to future epidemics. Besides, this study elucidated critical factors affecting early warnings based on WBE amid the COVID-19 pandemic.
The article is a continuation of research published by the author elsewhere (Śleszyński, 2020). The elaboration presents the regularity of spatial distribution of infections during the first six months after the detection of SARS-CoV-2 coronovirus in Poland under strong lockdown conditions. The main aim is to try to determine the basic temporal-spatial patterns and to answer the questions: to what extent the phenomenon was ordered and to what extent it was chaotic, whether there are any particular features of spread, whether the infection is concentrated or dispersed and whether the spreading factors in Poland are similar to those observed in other countries. Day by day data were used according to the counties collected in Rogalski’s team (2020). The data were aggregated to weekly periods (7 days) and then the regularity of spatial distribution was searched for using the cartogram method, time series shifts, rope correlation between the intensity of infections in different periods, Herfindahl-Hirschman concentration index (HHI) and cluster analysis. A spatial typology of infection development in the population was also performed. Among other things, it was shown that during the first period (about 100 days after the first case), the infections became more and more spatially concentrated and then dispersed. Differences were also shown in relation to the spread of the infection compared to observations from other countries, i.e. no relation to population density and level of urbanization.
On 30 December 2019, the Program for Monitoring Emerging Diseases notified the world about a pneumonia of unknown cause in Wuhan, China (1). Since then, scientists have made remarkable progress in understanding the causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), its transmission, pathogenesis, and mitigation by vaccines, therapeutics, and non-pharmaceutical interventions. Yet more investigation is still needed to determine the origin of the pandemic. Theories of accidental release from a lab and zoonotic spillover both remain viable. Knowing how COVID-19 emerged is critical for informing global strategies to mitigate the risk of future outbreaks.
This paper introduces new methods to analyze the changing progression of COVID-19 cases to deaths in different waves of the pandemic. First, an algorithmic approach partitions each country or state’s COVID-19 time series into a first wave and subsequent period. Next, offsets between case and death time series are learned for each country via a normalized inner product. Combining these with additional calculations, we can determine which countries have most substantially reduced the mortality rate of COVID-19. Finally, our paper identifies similarities in the trajectories of cases and deaths for European countries and U.S. states. Our analysis refines the popular conception that the mortality rate has greatly decreased throughout Europe during its second wave of COVID-19; instead, we demonstrate substantial heterogeneity throughout Europe and the U.S. The Netherlands exhibited the largest reduction of mortality, a factor of 16, followed by Denmark, France, Belgium, and other Western European countries, greater than both Eastern European countries and U.S. states. Some structural similarity is observed between Europe and the United States, in which Northeastern states have been the most successful in the country. Such analysis may help European countries learn from each other’s experiences and differing successes to develop the best policies to combat COVID-19 as a collective unit.
Human sewage from Florianopolis (Santa Catarina, Brazil) was analyzed for severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) from October 2019 until March 2020. Twenty five ml of sewage samples were clarified and viruses concentrated using a glycine buffer method coupled with polyethylene glycol precipitation, and viral RNA extracted using a commercial kit. SARS-CoV-2 RNA was detected by RT-qPCR using oligonucleotides targeting N1, S and two RdRp regions. The results of all positive samples were further confirmed by a different RT-qPCR system in an independent laboratory. S and RdRp amplicons were sequenced to confirm identity with SARS-CoV-2. Genome sequencing was performed using two strategies; a sequence-independent single-primer amplification (SISPA) approach, and by direct metagenomics using Illumina's NGS. SARS-CoV-2 RNA was detected on 27th November 2019 (5.49 ± 0.02 log10 SARS-CoV-2 genome copies (GC) L⁻¹), detection being confirmed by an independent laboratory and genome sequencing analysis. The samples in the subsequent three events were positive by all RT-qPCR assays; these positive results were also confirmed by an independent laboratory. The average load was 5.83 ± 0.12 log10 SARS-CoV-2 GC L⁻¹, ranging from 5.49 ± 0.02 log10 GC L⁻¹ (27th November 2019) to 6.68 ± 0.02 log10 GC L⁻¹ (4th March 2020). Our findings demonstrate that SARS-CoV-2 was likely circulating undetected in the community in Brazil since November 2019, earlier than the first reported case in the Americas (21st January 2020).
Using serum samples routinely collected in 9144 adults from a French general population-based cohort, we identified 353 participants with a positive anti-SARS-CoV-2 IgG test, among whom 13 were sampled between November 2019 and January 2020 and were confirmed by neutralizing antibodies testing. Investigations in 11 of these participants revealed experience of symptoms possibly related to a SARS-CoV-2 infection or situations at risk of potential SARS-CoV-2 exposure. This suggests early circulation of SARS-CoV-2 in Europe.
Background
Although the United States is among the world's countries with the highest mortalities of COVID-19, inadequate geospatial studies have analyzed the disease mortalities across the nation.
Methods
In this county-level study, we investigated age-adjusted co-mortalities of 20 diseases, including cardiovascular, cancer, drug and alcohol disorder, respiratory and infectious diseases with COVID-19 over the first ten months of epidemic. One-way analysis of variance was applied to the Local Moran's I classes (High-High and Low-Low clusters, and non-significant counties of COVID-19) to examine whether the mean mortality measures of covariates that fall into the classes are significantly different. Moreover, a mixed-effects multinomial logistic regression model was employed to estimate the effects of mortalities on COVID-19 classes.
Results
Results showed that the distribution of COVID-19 case fatality ratio (CFR) and mortality rate co-occurrence of High-High clusters were mainly concentrated in Louisiana, Connecticut, and New Jersey. Also, positive associations were observed between High-High cluster of COVID-19 CFR and Asthma (OR = 4.584, 95 % Confidence Interval (CI): 2.583–8.137), Hepatitis (OR = 5.602, CI: 1.265–24.814) and Leukemia (OR = 2.172, CI: 1.518–3.106) mortality rates compared to the non-significant counties, respectively.
Conclusions
Our findings imply that public health policymakers should pay careful attention to those counties with elevated pre-existing mortality rates when combating the COVID-19 outbreak. Future vaccine allocation and more medical professionals and treatment equipment should be a priority to those High-High clusters.
Objectives:
to describe the overall mortality increase in the provinces of Milan and Lodi - area covered by the Agency for Health Protection of Milan - during the COVID-19 epidemic in the first four months of 2020, compare it with the same time period in the years 2016-2019, and evaluate to what extent the mortality can be directly attributed to the outbreak.
Design:
cohort study.
Setting and participants:
using a new information system developed during the pandemic, we gathered data on the number of daily deaths in the population residing in the provinces of Milan and Lodi by Local Health Unit (ASST) and age groups. To describe the case fatality of COVID-19, we performed a record linkage with a database specially constructed during the epidemic to identify deaths that occurred in confirmed cases.
Main outcome measures:
mortality and excess mortality were analysed by comparing the number of observed deaths in the first 4 months of 2020 with the average deaths of the years 2016-2019 in the same calendar period and with expected deaths, estimated using a Poisson model. Furthermore, a measure of relative risk was calculated as observed/expected ratio with a 95% confidence interval.
Results:
the increase in mortality for all causes occurring in the study population in the first 4 months of 2020 was 48.8%, 30.8% for ages between 60 and 69, 43.9% for ages between 70 and 79, and 56.7% for subjects above 80 years of age. Focusing on the epidemic period, from 1 March to 30 April, the excess is quantifiable as more than 2-fold and mainly concerns the population over 60 years of age. The excess mortality was observed in all local health units (ASSTs). The highest increments were in the province of Lodi and the North-East of Milan (ASST Nord). In the ASSTs of Lodi and Melegnano-Martesana the mortality excess was detectable from March 15th, while for the other ASSTs the increase began in the first week of April.
Conclusions:
evaluation of overall mortality in the provinces of Milan and Lodi during the first wave of the Covid-19 epidemic showed a significant excess compared to the first 4 months of the years 2016-2019, mainly in the population over 60 years of age. However, this excess cannot be completely attributed directly to COVID-19 itself. This phenomenon was more intense in the Lodi ASST, with daily deaths up to 5 times higher than expected.
In this study, we aimed to examine spatial inequalities of COVID-19 mortality rate in relation to spatial inequalities of socioeconomic and environmental factors across England. Specifically, we first explored spatial patterns of COVID-19 mortality rate in comparison to non-COVID-19 mortality rate. Subsequently, we established models to investigate contributions of socioeconomic and environmental factors to spatial variations of COVID-19 mortality rate across England (N = 317). Two newly developed specifications of spatial regression models were established successfully to estimate COVID-19 mortality rate (R² = 0.49 and R² = 0.793). The level of spatial inequalities of COVID-19 mortality is higher than that of non-COVID-19 mortality in England. Although global spatial association of COVID-19 mortality and non-COVID-19 mortality is positive, local spatial association of COVID-19 mortality and non-COVID-19 mortality is negative in some areas. Expectedly, hospital accessibility is negatively related to COVID-19 mortality rate. Percent of Asians, percent of Blacks, and unemployment rate are positively related to COVID-19 mortality rate. More importantly, relative humidity is negatively related to COVID-19 mortality rate. Moreover, among the spatial models estimated, the ‘random effects specification of eigenvector spatial filtering model’ outperforms the ‘matrix exponential spatial specification of spatial autoregressive model’.
The main objective of this study is to investigate the relationship between the COVID-19 and the weather factors of the most populated and industrialized countries in Europe and propose the best mathematical model to forecast the daily number of COVID-19 cases. To find the relationship between the COVID-19 and the weather factors of absolute humidity and temperature in Spain, France, Italy, Germany, and the United Kingdom, we conducted a Poisson analysis. We also used the General Linear Neural Network (GRNN) model to forecast the trend and number of daily COVID-19 cases in these European countries. The results reveal a statistically significant negative relationship between the number of COVID-19 infections and weather factors of temperature & absolute humidity. Furthermore, the results show a stronger negative relationship between COVID-19 and absolute humidity than temperature. In our proposed GRNN method, we find better compatibility for the COVID-19 cases in Italy relative to the other European countries in this study.
Background:
There are still many unknowns about COVID-19. We do not know its exact mortality rate nor the speed through which it spreads across communities. This lack of evidence complicates the design of appropriate response policies.
Methods:
We source daily death registry data for 4100 municipalities in Italy's north and match them to Census data. We augment the dataset with municipality-level data on a host of co-factors of COVID-19 mortality, which we exploit in a differences-in-differences regression model to analyze COVID-19-induced mortality.
Results:
We find that COVID-19 killed more than 0.15% of the local population during the first wave of the epidemic. We also show that official statistics vastly underreport this death toll, by about 60%. Next, we uncover the dramatic effects of the epidemic on nursing home residents in the outbreak epicenter: in municipalities with a high share of the elderly living in nursing homes, COVID-19 mortality was about twice as high as in those with no nursing home intown.
Conclusions:
A pro-active approach in managing the epidemic is key to reduce COVID-19 mortality. Authorities should ramp-up testing capacity and increase contact-tracing abilities. Adequate protective equipment should be provided to nursing home residents and staff.
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease COVID-19, a public health emergency worldwide, and Italy is among the most severely affected countries. The first autochthonous Italian case of COVID-19 was documented on February 21, 2020. We investigated the possibility that SARS-CoV-2 emerged in Italy earlier than that date, by analysing 40 composite influent wastewater samples collected - in the framework of other wastewater-based epidemiology projects - between October 2019 and February 2020 from five wastewater treatment plants (WWTPs) in three cities and regions in northern Italy (Milan/Lombardy, Turin/Piedmont and Bologna/Emilia Romagna). Twenty-four additional samples collected in the same WWTPs between September 2018 and June 2019 (i.e. long before the onset of the epidemic) were included as ‘blank’ samples. Viral concentration was performed according to the standard World Health Organization procedure for poliovirus sewage surveillance, with modifications. Molecular analysis was undertaken with both nested RT-PCR and real-rime RT-PCR assays. A total of 15 positive samples were confirmed by both methods. The earliest dates back to 18 December 2019 in Milan and Turin and 29 January 2020 in Bologna. Virus concentration in the samples ranged from below the limit of detection (LOD) to 5.6 × 10⁴ genome copies (g.c.)/L, and most of the samples (23 out of 26) were below the limit of quantification of PCR.
Our results demonstrate that SARS-CoV-2 was already circulating in northern Italy at the end of 2019. Moreover, it was circulating in different geographic regions simultaneously, which changes our previous understanding of the geographical circulation of the virus in Italy. Our study highlights the importance of environmental surveillance as an early warning system, to monitor the levels of virus circulating in the population and identify outbreaks even before cases are notified to the healthcare system.
Purpose
This study aims to understand how spatial structures, the interconnections between counties, matter in understanding COVID-19 period prevalence across the US.
Methods
We assemble a county-level dataset that contains COVID-19 confirmed cases through June 28, 2020 and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in COVID-19 period prevalence.
Results
The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, model fit is conspicuous in its heterogeneity across counties.
Conclusions
Spatial models can help partially explain the geographic disparities in COVID-19 period prevalence. These models reveal spatial variability in model fit including identifying regions of the country where fit is heterogeneous and worth closer attention in the immediate short term.
The emergence of SARS-CoV-2 and the resulting disease, COVID-19, in China in late 2019 provides unique and sometimes overwhelming challenges to global public health and to the health and well-being of populations. The World Health Organization’s subsequent declaration of this outbreak as a public health emergency of international importance was, if anything, an understatement. The emergence of this pathogen is best seen as the latest and globally one of the most important examples of emerging infectious diseases – a phenomenon that has been at the forefront of global health for several decades. We trace the initial experience with COVID-19, highlighting experiences in Europe, Asia, and the Pacific. We emphasize selected countries, with varied demographic, socioeconomic, and political profiles, and identify instructive lessons from these national and regional experiences in terms of the efficacy of responses. Previous experience with serious epidemics of emerging infectious disease, well developed public health infrastructure, early, well-coordinated and transparent communication, rapidly established surveillance, case ascertainment, contact tracing, and containment are all vitally important in the control of this disease.
Among the majority of research on individual factors leading to coronavirus mortality, age has been identified as a dominant factor. Health and other individual factors including gender, comorbidity, ethnicity and obesity have also been identified by other studies. In contrast, we examine the role of economic structural factors on COVID-19 mortality rates. Particularly, focusing on a densely populated region of France, we document evidence that higher economic “precariousness indicators” such as unemployment and poverty rates, lack of formal education and housing are important factors in determining COVID-19 mortality rates. Our study will help inform policy makers regarding the role of economic factors in managing pandemics.