Heterogeneous Spread Research Articles

Variance of the Infection Number of Heterogeneous Malware Spread in Network

The Susceptible–Infected–Susceptible (SIS) model in complex networks is one of the critical models employed in the modeling of virus spread. The study of the heterogeneous SIS model with a non-homogeneous nodal infection rate in finite-size networks has attracted little attention. Investigating the statistical properties of heterogeneous SIS epidemic dynamics in finite networks is thus intriguing. In this paper, we focus on the measure of variability in the number of infected nodes for the heterogeneous SIS epidemic dynamics in finite-size bipartite graphs and star graphs. Specifically, we investigate the metastable-state variance of the number of infected nodes for the SIS epidemic process in finite-size bipartite graphs and star graphs with heterogeneous nodal infection rates. We employ an extended individual-based mean-field approximation to analyze the heterogeneous SIS epidemic process in finite-size bipartite networks and star graphs. We derive the approximation solutions of the variance of the infected number. We verify the proposed theory by simulations. The proposed theory has the potential to help us better understand the fluctuations of SIS models like epidemic dynamics with a non-homogeneous infection rate.

Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India

The spatio-temporal distribution of COVID-19 across India’s states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space–time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space–time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062–6.205) and deaths (RR: 2.477; 95% CI: 1.361–4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers’ rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space–time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

Effects of the COVID-19 pandemic on life expectancy and premature mortality in the German federal states in 2020 and 2021.

The mortality impact of COVID-19 has mainly been studied at the national level. However, looking at the aggregate impact of the pandemic at the country level masks heterogeneity at the subnational level. Subnational assessments are essential for the formulation of public health policies. This is especially important for federal countries with decentralised healthcare systems, such as Germany. Therefore, we assess geographical variation in the mortality impact of COVID-19 for the 16 German federal states in 2020 and 2021 and the sex differences therein. For this purpose, we adopted an ecological study design, using population-level mortality data by federal state, age, and sex, for 2005-2021 obtained from the German Federal Statistical Office. We quantified the impact of the pandemic using the excess mortality approach. We estimated period life expectancy losses (LE losses), excess premature mortality, and excess deaths by comparing their observed with their expected values. The expected mortality was based on projected age-specific mortality rates using the Lee-Carter methodology. Saxony was the most affected region in 2020 (LE loss 0.77 years, 95% CI 0.74;0.79) while Saarland was the least affected (-0.04, -0.09;0.003). In 2021, the regions with the highest losses were Thuringia (1.58, 1.54;1.62) and Saxony (1.57, 1.53;1.6) and the lowest in Schleswig-Holstein (0.13, 0.07;0.18). Furthermore, in 2021, eastern regions experienced higher LE losses (mean: 1.13, range: 0.85 years) than western territories (mean: 0.5, range: 0.72 years). The regional variation increased between 2020 and 2021, and was higher among males than among females, particularly in 2021. We observed an unequal distribution of the mortality impact of COVID-19 at the subnational level in Germany, particularly in 2021 among the male population. The observed differences between federal states might be partially explained by the heterogeneous spread of the virus in 2020 and by differences in the population's propensity to follow preventive guidelines.

Abstract 2203: Circulating rare events inform disease progression in patients with upper tract urothelial carcinoma undergoing surgery

Abstract The liquid biopsy presents potential as a non-invasive cancer screening tool, and its role in diagnosing and monitoring the progression of primary upper tract urothelial carcinoma (UTUC) to reveal the tumor’s kinetics remains to be fully understood. This study includes 22 patients diagnosed with UTUC, from which we collected 1 peripheral blood (PB) sample prior to surgery (pre-op; n = 22) and 1 sample at follow-up (post-op; n = 11). Each patient’s sample was analyzed via our enrichment-free third generation comprehensive High-Definition Single Cell Assay (HDSCA3.0) workflow to detect rare events in the PB. Rare events include circulating tumor cells (CTCs) and large extracellular vesicles (LEVs) and are identified via a 4-channel immunofluorescence assay that captures the following morphometric expressions: pan cytokeratin (CK), vimentin (V), CD45/CD31 (CD), and cellular nucleation (which is absent in LEVs) through DAPI (D). Through a matched sample analysis approach via a Wilcoxon signed-rank test, pre-op and post-op liquid biopsy profiles were compared to each other as well as to samples from 50 normal donors (NDs) who had no known pathology. The HDSCA3.0 analysis discovered a highly heterogeneous spread of rare events in PB samples collected from patients with UTUC in both pre-op and post-op time periods and in ND samples. For pre-op vs ND samples, significant differences were found in total rare events, total rare cells, events expressing D|V, D|CK|V|CD, D|V|CD, total LEVs, total CK+ cells, CK LEVs, and events expressing D and D|CK. For post-op vs ND samples, significant differences were in total rare events, total rare cells, events expressing D|V, D|CK|V|CD, total CK+ cells, and events expressing D|CK|V. For pre-op vs post-op samples, significant differences manifested in total LEVs, CK LEVs, and CK|CD LEVs. At follow-up, 3 patients had an increase in rare events, and 8 patients had a decrease in rare events, 4 of the 8 presenting with ND rare event levels. This study has the potential to inform how primary UTUC evolves in patients over time as well as elucidate if rare event expressions may correlate with specific disease states. By analyzing circulating rare events through channel-type classifications, different frequencies at various time points compared to ND samples can assist in clinical decision making for treating UTUC. Citation Format: Salmaan Sayeed, George Courcoubetis, Alireza Ghoreifi, Amy Huang, Jeremy Mason, Inderbir S. Gill, Peter Kuhn, Hooman Djaladat, Stephanie N. Shishido. Circulating rare events inform disease progression in patients with upper tract urothelial carcinoma undergoing surgery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2203.

Multi-Competitive Virus Spread over a Time-Varying Networked SIS Model with an Infrastructure Network

We study the spread of multi-competitive viruses over a (possibly) time-varying network of individuals accounting for the presence of shared infrastructure networks that further enables transmission of the virus. We establish a sufficient condition for exponentially fast eradication of a virus for: 1) time-invariant graphs, 2) time-varying graphs with symmetric interactions between individuals and homogeneous virus spread across the network (same healing and infection rate for all individuals), and 3) directed and slowly varying graphs with heterogeneous virus spread (not necessarily same healing and infection rates for all individuals) across the network. Numerical examples illustrate our theoretical results and indicate that, for the time-varying case, violation of the aforementioned sufficient conditions could lead to the persistence of a virus.

Risk Factors Associated with the Spatiotemporal Spread of the SARS-CoV-2 Omicron BA.2 Variant - Shanghai Municipality, China, 2022.

Previous studies have explored the spatial transmission patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and have assessed the associated risk factors. However, none of these studies have quantitatively described the spatiotemporal transmission patterns and risk factors for Omicron BA.2 at the micro (within-city) scale. This study highlights the heterogeneous spread of the 2022 Omicron BA.2 epidemic in Shanghai, and identifies associations between different metrics of spatial spread at the subdistrict level and demographic and socioeconomic characteristics of the population, human mobility patterns, and adopted interventions. Disentangling different risk factors might contribute to a deeper understanding of the transmission dynamics and ecology of coronavirus disease 2019 and an effective design of monitoring and management strategies.

County-Level Sociodemographic Factors Associated with COVID-19 Incidence and Mortality in North and South Carolina.

BACKGROUND There is limited research regarding associations between county-level factors and COVID-19 incidence and mortality. While the Carolinas are geographically connected, they are not homogeneous, with statewide political and intra-state socioeconomic differences leading to heterogeneous spread between and within states.METHODS Infection and mortality data from Johns Hopkins University during the 7 months since the first reported case in the Carolinas was combined with county-level socioeconomic/demographic factors. Time series imputations were performed whenever county-level reported infections were implausible. Multivariate Poisson regression models were fitted to extract incidence (infection and mortality) rate ratios by county-level factor. State-level differences in filtered trends were also calculated. Geospatial maps and Kaplan-Meier curves were constructed stratifying by median county-level factor. Differences between North and South Carolina were identified.RESULTS Incidence and mortality rates were lower in North Carolina than South Carolina. Statistically significant higher incidence and mortality rates were associated with counties in both states with higher proportions of Black/African American populations and those without health insurance aged < 65 years. Counties with larger populations aged ≥ 75 years were associated with increased mortality (but decreased incidence) rates.LIMITATIONS COVID-19 data contained multiple inconsistencies, so imputation was needed, and covariate-based data was not synchronous and potentially insufficient in granularity given the epidemiology of the disease. County-level analyses imply within-county homogeneity, an assumption increasingly breached by larger counties.CONCLUSION While statewide interventions were initially implemented, inter-county racial/ethnic and socioeconomic variability points to the need for more heterogeneous interventions, including policies, as populations within particular counties may be at higher risk.

COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level.

COVID-19 evolution imposes significant challenges for the European healthcare system. The heterogeneous spread of the pandemic within EU regions elicited a wide range of policies, such as school closure, transport restrictions, etc. However, the implementation of these interventions is not accompanied by the implementation of quantitative methods, which would indicate their effectiveness. As a result, the efficacy of such policies on reducing the spread of the virus varies significantly. This paper investigates the effectiveness of using deep learning paradigms to accurately model the spread of COVID-19. The deep learning approaches proposed in this paper are able to effectively map the temporal evolution of a COVID-19 outbreak, while simultaneously taking into account policy interventions directly into the modelling process. Thus, our approach facilitates data-driven decision making by utilizing previous knowledge to train models that predict not only the spread of COVID-19, but also the effect of specific policy measures on minimizing this spread. Global models at the EU level are proposed, which can be successfully applied at the national level. These models use various inputs in order to successfully model the spatio-temporal variability of the phenomenon and obtain generalization abilities. The proposed models are compared against the traditional epidemiological and Autoregressive Integrated Moving Average (ARIMA) models.

Predicting spatially heterogeneous invasive spread: Pyracantha angustifolia invading a dry Andean valley in northern Argentina

Understanding the drivers of invasive species spread is key to designing optimal management programmes for controlling them. Population models, parameterized from demographic and dispersal data, are useful for simulating invasion when long-term observations are lacking, which is particularly important when invasions are occurring rapidly. We aimed to understand how the spread of Pyracantha angustifolia (an invasive ornamental shrub) is influenced by heterogeneity in local conditions within a dry inter-montane valley in northwestern Argentina that is currently experiencing rapid urban expansion and concomitant reduction in cattle farming. Field surveys demonstrated that individuals growing in shrublands have a higher fecundity than those in grassland and rocky habitats. Furthermore, grazing pressure of cattle and horses substantially reduces fecundity and impacts the relationship between age and fecundity. We incorporated our field-estimated habitat and age-specific fecundities into both analytical integro-difference equation models and individual-based models to predict rates of spread across each habitat type in the presence or absence of grazing pressure. Results indicate that the rate of spread would be substantially higher in shrublands (by up to 33%) and highlight that grazing pressure can, through direct consumption of seeds by livestock, substantially depress the rate of spread (by up to 53%). These results suggest that shrubland areas in the valley are most vulnerable to invasion and that grazing may help to reduce the impact of Pyracantha. This suggests that a synergism between urban expansion and reduction in domestic livestock density contributes to the spread of an invasive species in this environment.

Funds and Investment Management of Virudhunagar Cooperative Urban Bank LTD

There are many Primary Urban Cooperative Banks (UCBs), forming a heterogeneous size and spread group. Many of these banks are very small in size and reach. They compete with more significant participants in the same banking space. Over the years, many urban cooperative banks became weak and non-viable, thus posing a systemic risk to the UCB sector. They lack avenues for raising capital funds since they cannot go for public issues of shares, nor can they issue shares to members at a premium. At the same time, there are many UCBs in the sector which is financially solid and viable. This sector in the recent past has witnessed significant developments that have far-reaching implications for the banking sector that is savings banking interest rate has been deregulated. It shows that the RBI gives a lot of freedom and autonomy for the smooth functioning of the UCBs. Hence, the virudhunagar UCB, by using independence and liberty, should bring in qualitative improvement in the method of functioning and altitudinal transformation and increase in per-employee Productivity, which will result in achieving the objectives of inclusive growth and sustainable development.This study, therefore, analyses Funds and Investment Management of Virudhunagar Urban Bank.

Evaluation of the measurement performance of water meters depending on water quality

Abstract Water meters of different types and sizes are used to monitor and bill the water supply. Although the water is of drinking water quality, its chemo-physical properties often adversely affect the measuring behaviour of a meter after a while. There is thus the risk that they no longer meet legal requirements and may no longer be used. In this paper a test regime with a focus on pH, total hardness and particle load is presented which allows water meters to be tested closer to their operating conditions prior to placing them on the market. The regime goes beyond the conventional continuous durability test as described in OIML R49:2013(E) and ISO 4064:2014. The feasibility and reliability of the test regime has been demonstrated through implementation at different facilities. In the study, the measurement performance of water meters of various types and from different manufacturers was also investigated. A heterogeneous spread of measurement errors was found for both, water meters in mint conditions and those which were exposed to a defined water quality. Furthermore, compared to the conventional continuous durability test, the test regime developed in the study generally leads to stronger changes in the measurement error of the water meters.

Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study

BackgroundGeographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known.MethodsWe conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population.ResultsAnalysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties.ConclusionsCounty-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

Pandemic spread in communities via random graphs

Working in the multi-type Galton–Watson branching-process framework we analyse the spread of a pandemic via a general multi-type random contact graph. Our model consists of several communities, and takes, as input, parameters that outline the contacts between individuals in distinct communities. Given these parameters, we determine whether there will be an outbreak and if yes, we calculate the size of the giant-connected-component of the graph, thereby, determining the fraction of the population of each type that would be infected before it ends. We show that the pandemic spread has a natural evolution direction given by the Perron–Frobenius eigenvector of a matrix whose entries encode the average number of individuals of one type expected to be infected by an individual of another type. The corresponding eigenvalue is the basic reproduction number of the pandemic. We perform numerical simulations that compare homogeneous and heterogeneous spread graphs and quantify the difference between them. We elaborate on the difference between herd immunity and the end of the pandemic and the effect of countermeasures on the fraction of infected population.

The importance and status of the micronutrient selenium in South Africa: a review.

Selenium (Se) is a vital micronutrient with widespread biological action but leads to toxicity when taken in excessive amounts. The biological benefits of Se are mainly derived from its presence in active sites of selenoproteins such as glutathione peroxidase (GPx). An enzyme whose role is to protect tissues against oxidative stress by catalysing the reduction of peroxidase responsible for various forms of cellular damage. The benefits of Se can be harvested when proper regulations of its intake are used. In South Africa, Se distribution in people's diets and animals are low with socio-economic factors and heterogeneous spread of Se in soil throughout the country playing a significant role. The possible causes of low Se in soils may be influenced by underlying geological material, climatic conditions, and anthropogenic activities. Sedimentary rock formations show higher Se concentrations compared to igneous and metamorphic rock formations. Higher Se concentrations in soils dominates in humid and sub-humid areas of South Africa. Furthermore, atmospheric acid deposition dramatically influences the availability of Se to plants. The studies reviewed in this article have shown that atomic absorption spectroscopy (AAS) is the most utilised analytical technique for total Se concentration determination in environmental samples and there is a lack of speciation data for Se concentrations. Shortcomings in Se studies have been identified, and the future research directions of Se in South Africa have been discussed.

On the heterogeneous spread of COVID-19 in Chile.

Non-pharmaceutical interventions (NPIs) have played a crucial role in controlling the spread of COVID-19. Nevertheless, NPI efficacy varies enormously between and within countries, mainly because of population and behavioral heterogeneity. In this work, we adapted a multi-group SEIRA model to study the spreading dynamics of COVID-19 in Chile, representing geographically separated regions of the country by different groups. We use national mobilization statistics to estimate the connectivity between regions and data from governmental repositories to obtain COVID-19 spreading and death rates in each region. We then assessed the effectiveness of different NPIs by studying the temporal evolution of the reproduction number . Analysing data-driven and model-based estimates of , we found a strong coupling of different regions, highlighting the necessity of organized and coordinated actions to control the spread of SARS-CoV-2. Finally, we evaluated different scenarios to forecast the evolution of COVID-19 in the most densely populated regions, finding that the early lifting of restriction probably will lead to novel outbreaks.

A structured model for COVID-19 spread: modelling age and healthcare inequities.

We use a stochastic branching process model, structured by age and level of healthcare access, to look at the heterogeneous spread of COVID-19 within a population. We examine the effect of control scenarios targeted at particular groups, such as school closures or social distancing by older people. Although we currently lack detailed empirical data about contact and infection rates between age groups and groups with different levels of healthcare access within New Zealand, these scenarios illustrate how such evidence could be used to inform specific interventions. We find that an increase in the transmission rates among children from reopening schools is unlikely to significantly increase the number of cases, unless this is accompanied by a change in adult behaviour. We also find that there is a risk of undetected outbreaks occurring in communities that have low access to healthcare and that are socially isolated from more privileged communities. The greater the degree of inequity and extent of social segregation, the longer it will take before any outbreaks are detected. A well-established evidence for health inequities, particularly in accessing primary healthcare and testing, indicates that Māori and Pacific peoples are at a higher risk of undetected outbreaks in Aotearoa New Zealand. This highlights the importance of ensuring that community needs for access to healthcare, including early proactive testing, rapid contact tracing and the ability to isolate, are being met equitably. Finally, these scenarios illustrate how information concerning contact and infection rates across different demographic groups may be useful in informing specific policy interventions.

Understanding Bactrocera dorsalis trapping to calibrate area‐wide management

AbstractKnowing the dispersal of released insects and estimating the size of field populations are keys to the successful implementation of area‐wide management (AWM) programmes based on the sterile insect technique (SIT), as they determine the release strategy of sterile males. Mark–release–recapture (MRR) is a common method used to estimate field populations and spatiotemporal dynamics. However, the extent to which the pest is attracted to lures is often difficult to identify, thereby biasing extrapolation to movement patterns and population size. We performed MRR experiments on the Oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), in a fruit‐growing area in Senegal. Methyl eugenol and protein baits were used to trap males and females, respectively. After studying the suitability of marking B. dorsalis with fluorescent pigments at the laboratory, two releases of marked sterile flies were organized in the centre of an organic mango orchard, first in the late mango fruiting stage and second in the fruit setting stage. Traps were placed symmetrically along a 250 and 500 m radius from the release point. A very small fraction of the released individuals was recaptured: 0.45% in the first release and 3.08% in the second. Trapping of both sterile and wild flies was completely anisotropic. Sterile flies were mostly trapped at a relatively short distance (250 m) from the release point, in the first two days. Male trapping using methyl eugenol was highly effective, whereas the response of females to food bait traps was low. The direction of the wind was the main driver of recapture, with flies heading upwind. The results underline the importance of taking the odour plume around the traps into account when estimating populations, and the heterogeneous spread of the wild population in the landscape for the set‐up of the release strategy of sterile insects for SIT‐based AWM.

Mortality Predictors for 221,414 COVID-19 Cases: The Role of Socio-Demographic Factors, Symptoms, and Comorbidities in a Brazilian State-Wide Landscape

Background: Brazil is facing a surge in COVID-19 infection, with heterogeneous regional spread associated with varying infection and death rates across the country’s states. This study aimed to thoroughly analyze the association of death with demographic characteristics, symptoms, and comorbidities at three levels of COVID-19 severity—in non-hospitalized, non-ICU-ward, and ICU-ward patients—in a state-wide context in South Brazil.Methods: We selected 221,414 cases in the state of Paraná diagnosed by positive RT-PCR tests, defining outcome based on electronic medical records including demographic characteristics (sex, age, HDI), description of symptoms (114,025 cases with at least one symptom—SY group), and comorbidities (116,229 cases with at least one comorbidity—CM group). Logistic regression was performed to test the association between deaths with demographic characteristics and categorical variables in three levels of medical intervention within the SY and CM groups.Findings: Registered ICU deaths ranged between 68·62% (SY) and 69·6% (CM). Males presented increased mortality among non-hospitalized patients (SY: OR=1·81, 95% CI=1·51–2·17; CM: OR=1.97, 95% CI=1·66–2·34) and non-ICU patients (SY: OR=1·24, 95% CI=1·07–1·45; CM: OR=1·35, 95% CI=1·15–1·57). Lower human development index (HDI) was associated with increased mortality in non-hospitalized patients (SY: OR=0·01, 95% CI=0·02–0·08, CM: OR=0·02, 95% CI=0·01– 0·11). A higher mortality rate occurred in patients older than 40 years (non-hospitalized OR=25·65, 95% CI=8·17–155·36, non-ICU OR=5·05, 95% CI=1·87–20·75, and ICU OR=3·91, 95% CI=1·27–14·58), with strong association in elderly SY and CM adults. The main symptoms associated with increased mortality were dyspnoea in non-hospitalized (OR=3·71, 95% CI=3·10–4·44), non-ICU (OR=2·47, 95% CI=2·11–2·90), and ICU patients (OR=1·43, 95% CI=1·16–1·77). In all patients, diabetes, heart, neurological, and kidney diseases were associated with death (e.g., in non-hospitalized patients: OR=2·12, 95% CI=1·72–2·60; OR=1·84, 95% CI=1·47–2·29; OR=2·18, 95% CI=1·46–3·17 OR=2·00, 95% CI=1·20–3·19, respectively).Interpretation: The demographic characteristics “male sex” and “age > 40 years” were unequivocally associated with death and with lower HDI in non-hospitalized patients. Other categorical variables—dyspnoea, diabetes, heart disease, neurological disease, and kidney disease—were associated with most patients’ mortality. Our study highlights the importance of these predictors for the implementation of public healthcare policy in response to the pandemic.Funding: Araucária Foundation – FAAP-PR, State Secretariat of Science, Technology and Higher Education – SETI –PR. ABWB currently receives a research productivity scholarship from CNPq (314288/2018-0).Declaration of Interests: We declare no competing interests.Ethics Approval Statement: The study received the ethical approval number CAAE 39108020.0.0000.0107, registered on the Plataforma Brasil – Comissão Nacional de Ética em Pesquisa database (https://plataformabrasil.saude.gov.br).

Prolong Network Lifetime in the Wireless Sensor Networks: An Improved Approach

This paper presents a novel approach for prolonging the network lifetime and reducing the energy being consumed within the wireless sensor networks (WSN). The sensor nodes are distributed in the geographical area in a heterogeneous manner and spread in an advanced and normal mode with high and low energy, respectively. All sensor nodes are organized in groups or clusters. Every cluster has a cluster head (CH) node, and every CH gathers the information from all the sensor nodes, which will be communicated to the base station (BS). Sensor nodes are positioned with initial energy. The network's lifetime and consumed energy are both essential parameters in contrast with the existing routing protocols. It has been accomplished by choosing the proper cluster heads. The mathematical model and simulation results obtained on MATLAB 2015b show a significant fall of the total energy being consumed, now standing at 35%, with the lifetime of the network having been enhanced by 80% as compared to LEACH, Modified-LEACH, and SEP protocol.

Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods

The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied.