Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 1.354
Filter
1.
Int J Environ Res Public Health ; 19(20)2022 Oct 14.
Article in English | MEDLINE | ID: covidwho-2068512

ABSTRACT

Epidemics represent a threat to human life and economy. Meanwhile, medical and non-medical approaches to fight against them may result in additional economic shocks. In this paper, we examine the economic impact of the 2003 SARS outbreak in China and associated government policies. Although the epidemic caused a substantial economic loss in the short term, the interventions for medical purposes positively impacted the economy of the severely affected regions through the increase in investments such as other fiscal stimuli. There is strong and robust evidence suggesting that the SARS epidemic and its associated countermeasure policies boosted local output by around 4% and industrial production by around 5%. The positive growth was mainly derived from the increase in investment and government activity, especially government expenditure. Besides that, lagged impacts were particularly pronounced to the economic system and lasted for longer even than the epidemic period in a biological sense. We attribute this to the relatively aggressive stance of policymakers in the face of the epidemic situation.


Subject(s)
Epidemics , Severe Acute Respiratory Syndrome , Humans , Severe Acute Respiratory Syndrome/epidemiology , Disease Outbreaks , China/epidemiology , Government , Economic Development
2.
Lancet ; 400(10365): 1803-1820, 2022 11 19.
Article in English | MEDLINE | ID: covidwho-2159959

ABSTRACT

Type 2 diabetes accounts for nearly 90% of the approximately 537 million cases of diabetes worldwide. The number affected is increasing rapidly with alarming trends in children and young adults (up to age 40 years). Early detection and proactive management are crucial for prevention and mitigation of microvascular and macrovascular complications and mortality burden. Access to novel therapies improves person-centred outcomes beyond glycaemic control. Precision medicine, including multiomics and pharmacogenomics, hold promise to enhance understanding of disease heterogeneity, leading to targeted therapies. Technology might improve outcomes, but its potential is yet to be realised. Despite advances, substantial barriers to changing the course of the epidemic remain. This Seminar offers a clinically focused review of the recent developments in type 2 diabetes care including controversies and future directions.


Subject(s)
Diabetes Mellitus, Type 2 , Epidemics , Humans , Child , Young Adult , Adult , Diabetes Mellitus, Type 2/drug therapy , Diabetes Mellitus, Type 2/epidemiology , Pharmacogenetics , Precision Medicine , Technology
3.
Zhonghua Liu Xing Bing Xue Za Zhi ; 43(11): 1699-1704, 2022 Nov 10.
Article in Chinese | MEDLINE | ID: covidwho-2143855

ABSTRACT

Objective: To clarify the epidemiological characteristics and spatiotemporal clustering dynamics of COVID-19 in Shanghai in 2022. Methods: The COVID-19 data presented on the official websites of Municipal Health Commissions of Shanghai during March 1, 2022 and May 31, 2022 were collected for a spatial autocorrelation analysis by GeoDa software. A logistic growth model was used to fit the epidemic situation and make a comparison with the actual infection situation. Results: Pudong district had the highest number of symptomatic and asymptomatic infectants, accounting for 29.30% and 35.58% of the total infectants. Differences in cumulative attack rates and infection rates among 16 districts (P<0.001) were significant. The rates were significantly higher in Huangpu district than in other districts. The attack rate of COVID-19 from March 1, 2022 to May 31, 2022 had a global spatial positive correlation (P<0.05). Spatial distribution of COVID-19 attack rate was different at different periods. The global autocorrelation coefficient from March 16 to March 29, April 6 to April 12 and May 18 to May 24 had no statistical significance (P>0.05). Our local autocorrelation analysis showed that 22 high-high clustering areas were detected in eight periods.The high-risk hot-spot areas have experienced a "less-more-less" change process. The growth model fitting results were consistent with the actual infection situation. Conclusion: There was a clear spatiotemporal correlation in the distribution of COVID-19 in Shanghai. The comprehensive prevention and control measures of COVID-19 epidemic in Shanghai have effectively prohibited the growth of the epidemic, not only curbing the spatially spread of high-risk epidemic areas, but also reducing the risk of transmission to other cities.


Subject(s)
COVID-19 , Epidemics , Humans , COVID-19/epidemiology , China/epidemiology , Disease Outbreaks , Spatial Analysis
4.
Int J Public Health ; 67: 1605177, 2022.
Article in English | MEDLINE | ID: covidwho-2142395

ABSTRACT

Objectives: Waves of epidemics associated with Omicron variant of Coronavirus Disease 2019 (COVID-19) in major cities in China this year have been controlled. It is of great importance to study the transmission characteristics of these cases to support further interventions. Methods: We simulate the transmission trajectory and analyze the intervention influences of waves associated with Omicron variant in major cities in China using the Suspected-Exposed-Infectious-Removed (SEIR) model. In addition, we propose a model using a function between the maximum daily infections and the duration of the epidemic, calibrated with data from Chinese cities. Results: An infection period of 5 days and basic reproduction number R0 between 2 and 8.72 are most appropriate for most cases in China. Control measures show a significant impact on reducing R0, and the earlier control measures are implemented, the shorter the epidemic will last. Our proposed model performs well in predicting the duration of the epidemic with an average error of 2.49 days. Conclusion: Our results show great potential in epidemic model simulation and predicting the end date of the Omicron epidemic effectively and efficiently.


Subject(s)
COVID-19 , Epidemics , Humans , COVID-19/epidemiology , Cities/epidemiology , SARS-CoV-2 , China/epidemiology
5.
Sci Rep ; 12(1): 20499, 2022 Nov 28.
Article in English | MEDLINE | ID: covidwho-2133631

ABSTRACT

The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users' trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.


Subject(s)
COVID-19 , Epidemics , Names , Humans , COVID-19/epidemiology , China/epidemiology , Machine Learning
6.
Epidemiol Infect ; 150: e171, 2022 Sep 27.
Article in English | MEDLINE | ID: covidwho-2133093

ABSTRACT

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9-80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0-34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.


Subject(s)
COVID-19 , Epidemics , Humans , Middle Aged , Computer Simulation , COVID-19/epidemiology , COVID-19/transmission , Disease Outbreaks , SARS-CoV-2 , Asymptomatic Infections
7.
Sci Rep ; 12(1): 19780, 2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2119290

ABSTRACT

Human behavioural changes are poorly understood, and this limitation has been a serious obstacle to epidemic forecasting. It is generally understood that people change their respective behaviours to reduce the risk of infection in response to the status of an epidemic or government interventions. We must first identify the factors that lead to such decision-making to predict these changes. However, due to an absence of a method to observe decision-making for future behaviour, understanding the behavioural responses to disease is limited. Here, we show that accommodation reservation data could reveal the decision-making process that underpins behavioural changes, travel avoidance, for reducing the risk of COVID-19 infections. We found that the motivation to avoid travel with respect to only short-term future behaviours dynamically varied and was associated with the outbreak status and/or the interventions of the government. Our developed method can quantitatively measure and predict a large-scale population's behaviour to determine the future risk of COVID-19 infections. These findings enable us to better understand behavioural changes in response to disease spread, and thus, contribute to the development of reliable long-term forecasting of disease spread.


Subject(s)
COVID-19 , Epidemics , Humans , COVID-19/epidemiology , Travel , Disease Outbreaks/prevention & control , Forecasting
8.
Proc Natl Acad Sci U S A ; 119(47): e2213879119, 2022 Nov 22.
Article in English | MEDLINE | ID: covidwho-2119191

ABSTRACT

The main mathematical result in this paper is that change of variables in the ordinary differential equation (ODE) for the competition of two infections in a Susceptible-Infected-Removed (SIR) model shows that the fraction of cases due to the new variant satisfies the logistic differential equation, which models selective sweeps. Fitting the logistic to data from the Global Initiative on Sharing All Influenza Data (GISAID) shows that this correctly predicts the rapid turnover from one dominant variant to another. In addition, our fitting gives sensible estimates of the increase in infectivity. These arguments are applicable to any epidemic modeled by SIR equations.


Subject(s)
COVID-19 , Epidemics , Influenza, Human , Humans , SARS-CoV-2/genetics , Disease Susceptibility
10.
Travel Med Infect Dis ; 50: 102484, 2022.
Article in English | MEDLINE | ID: covidwho-2118922

ABSTRACT

BACKGROUND: We aimed to calculate the weekly growth of the incidence and the effective reproductive number (Rt) of the 2022 Monkeypox epidemic during its introduction in Brazil. METHOD: We described the case distribution in the country and calculated the incidence trend and the Rt in the four geographical states with the highest case reports. By using two regression approaches, count model and the Prais-Winsten, we calculated the relative incidence increase. Moreover, we estimated the Rt for the period between the 24th and the 50th days after the first official report, using a serial interval reported in another population and two alternative values (± 3 days). RESULTS: Up to August 22, 3.896 Monkeypox cases were confirmed in Brazil. The weekly incidence increases were between 37.5% (95% CI: 20.7% - 56,6%) and 82.1% (95% CI: 59.5%-107.8%), and all estimates of Rt were significantly higher than 1 in the four states analyzed. CONCLUSIONS: The Monkeypox outbreak in Brazil is a significant public health emergency that requires coordinated public health strategies such as testing, contact tracing, and vaccination.


Subject(s)
Epidemics , Monkeypox , Humans , Monkeypox/epidemiology , Brazil/epidemiology , Incidence , Disease Outbreaks
11.
BMC Public Health ; 22(1): 2098, 2022 11 17.
Article in English | MEDLINE | ID: covidwho-2117061

ABSTRACT

BACKGROUND: With the prompt administration of coronavirus disease 2019 (COVID-19) vaccines, highly vaccinated countries have begun to lift their stringent control measures. However, considering the spread of highly transmissible new variants, resuming socio-economic activities may lead to the resurgence of incidence, particularly in nations with a low proportion of individuals who have natural immunity. Here, we aimed to quantitatively assess an optimal COVID-19 exit strategy in the Republic of Korea, where only a small number of cumulative incidences have been recorded as of September 2021, comparing epidemiological outcomes via scenario analysis. METHODS: A discrete-time deterministic compartmental model structured by age group was used, accounting for the variant-specific transmission dynamics and the currently planned nationwide vaccination. All parameters were calibrated using comprehensive empirical data obtained from the Korea Disease Control and Prevention Agency. RESULTS: Our projection suggests that tapering the level of social distancing countermeasures to the minimum level from November 2021 can efficiently suppress a resurgence of incidence given the currently planned nationwide vaccine roll-out. In addition, considering the spread of the Delta variant, our model suggested that gradual easing of countermeasures for more than 4 months can efficiently withstand the prevalence of severe COVID-19 cases until the end of 2022. CONCLUSIONS: Our model-based projections provide evidence-based guidance for an exit strategy that allows society to resume normal life while sustaining the suppression of the COVID-19 epidemic in countries where the spread of COVID-19 has been well controlled.


Subject(s)
COVID-19 , Epidemics , Humans , COVID-19/epidemiology , COVID-19/prevention & control , SARS-CoV-2 , Vaccination
12.
Acta Biotheor ; 71(1): 2, 2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2116399

ABSTRACT

A modified version of the SEIR model with the effects of vaccination and inter-state movement is proposed to simulate the spread of COVID-19 in Malaysia. A mathematical analysis of the proposed model was performed to derive the basic reproduction number. To enhance the model's forecasting capabilities, the model parameters were estimated using the Nelder-Mead simplex method by fitting the model outputs to the observed data. Our results showed a good fit between the model outputs and available data, where the model was then able to perform short-term predictions. In line with the rapid vaccination program, our model predicted that the COVID-19 cases in the country would decrease by the end of August. Furthermore, our findings indicated that relaxing travel restrictions from a highly vaccinated region to a low vaccinated region would result in an epidemic outbreak.


Subject(s)
COVID-19 , Epidemics , Animals , COVID-19/epidemiology , COVID-19/prevention & control , Malaysia/epidemiology , Travel , Vaccination/methods
13.
Int J Environ Res Public Health ; 19(22)2022 Nov 15.
Article in English | MEDLINE | ID: covidwho-2116089

ABSTRACT

Industrial parks are functional urban areas that carry the capacity to support highly concentrated production activities. The robustness and anti-interference ability of these areas are of great importance to maintaining economic vitality of a country. Focusing on the rate of production recovery (RPR), this paper examines the recovery of 436 major industrial parks in mainland China during the first wave of COVID-19. Leveraging spatio-temporal big data, we measured 14 attributes pertaining to industrial parks, covering four categories, namely spatial location, central city, park development, and public service. We focused on the spatial association and heterogeneity of the recovery patterns and identified the factors that truly affected the recovery of industrial parks with quantitative evaluation of their effects. The results reveal that: (1) RPR of industrial parks are significantly spatially clustered, with an obvious "cold spot" in the early outbreak area of Hubei Province and a prominent "center-periphery" pattern in developed areas, which is highly correlated with the spread of the epidemic. (2) The mechanisms driving the resumption of industrial parks are complex and versatile. All four categories in the variable matrix are related to RPR, including up to eight effective influencing factors. The effect of influencing factors is spatially heterogeneous, and its intensity varies significantly across regions. What is more interesting is that some impact factors show positive effects in some industrial parks while inhibiting the recovery in others. On the basis of the discussion of those findings with practical experiences, the planning and construction strategies of industrial park are suggested to mitigate the impact of similar external shocks.


Subject(s)
COVID-19 , Epidemics , Humans , COVID-19/epidemiology , Industry , Disease Outbreaks , China/epidemiology
14.
Int J Environ Res Public Health ; 19(22)2022 Nov 19.
Article in English | MEDLINE | ID: covidwho-2115998

ABSTRACT

In the post-epidemic era, China's urban communities are at the forefront of implementing the whole chain of accurate epidemic prevention and control. However, the uncertainty of COVID-19, the loopholes in community management and people's overly optimistic judgment of the epidemic have led to the frequent rebound of the epidemic and serious consequences. Existing studies have not yet formed a panoramic framework of community anti-epidemic work under the concept of resilience. Therefore, this article first summarizes the current research progress of resilient communities from three perspectives, including ideas and perspectives, theories and frameworks and methods and means, and summarizes the gap of the current research. Then, an innovative idea on the epidemic resilience of urban communities in China is put forward: (1) the evolution mechanism of community anti-epidemic resilience is described through the change law of dynamic networks; (2) the anti-epidemic resilience of urban communities is evaluated or predicted through the measurement criteria; (3) a simulation platform based on Multi-Agent and dynamic Bayesian networks simulates the interactive relationship between "epidemic disturbance-cost constraint--epidemic resilience"; (4) the anti-epidemic strategies are output intelligently to provide community managers with decision-making opinions on community epidemic prevention and control.


Subject(s)
COVID-19 , Epidemics , Humans , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , Epidemics/prevention & control , China/epidemiology
15.
Acta Biotheor ; 71(1): 1, 2022 Nov 15.
Article in English | MEDLINE | ID: covidwho-2112733

ABSTRACT

We propose a framework for the description of the effects of vaccinations on the spreading of an epidemic disease. Different vaccines can be dosed, each providing different immunization times and immunization levels. Differences due to individuals' ages are accounted for through the introduction of either a continuous age structure or a discrete set of age classes. Extensions to gender differences or to distinguish fragile individuals can also be considered. Within this setting, vaccination strategies can be simulated, tested and compared, as is explicitly described through numerical integrations.


Subject(s)
Communicable Diseases , Epidemics , Vaccines , Animals , Vaccination , Immunization
16.
Int J Infect Dis ; 111: 100-107, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-2113729

ABSTRACT

Background  COVID-19 was first detected in Wuhan, China, in 2019 and spread worldwide within a few weeks. The COVID-19 epidemic started to gain traction in France in March 2020. Subnational hospital admissions and deaths were then recorded daily and served as the main policy indicators. Concurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. Contrarily to individual tracking data, these can be a proxy for human contact networks between subnational administrative units. Methods  Motivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between July 2020 and April 2021. We added human contact network analytics, such as clustering coefficients, contact network strength, null links or curvature, as regressors. Findings  We found that predictions can be improved substantially (by more than 50%) at both the national level and the subnational level for up to 2 weeks. Our subnational analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. This original application of network analytics from colocalization data to epidemic spread opens new perspectives for epidemic forecasting and public health.


Subject(s)
COVID-19 , Epidemics , Hospitals , Humans , Incidence , SARS-CoV-2
17.
Nat Commun ; 13(1): 6218, 2022 Oct 20.
Article in English | MEDLINE | ID: covidwho-2103058

ABSTRACT

The dynamics of epidemic spreading is often reduced to the single control parameter R0 (reproduction-rate), whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, R0 may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the inter-host network spreading patterns with the intra-host evolutionary dynamics. We find that even in the extreme case when these two process are driven by mutually independent selection forces, mutations can still fundamentally alter the pandemic phase-diagram. The pandemic transitions, we show, are now shaped, not just by R0, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can breakthrough to gain widespread prevalence.


Subject(s)
Epidemics
18.
Bull Math Biol ; 84(12): 144, 2022 11 05.
Article in English | MEDLINE | ID: covidwho-2102924

ABSTRACT

It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here, we explore a complementary trend: the pathogen itself might experience a force of selection to become less "visible," or less "symptomatic," in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.


Subject(s)
Epidemics , Models, Biological , Humans , Mathematical Concepts
19.
Stud Health Technol Inform ; 299: 235-241, 2022 Nov 03.
Article in English | MEDLINE | ID: covidwho-2099074

ABSTRACT

The spread of a new coronavirus infection in the last two years together with HIV infection preserves and even increases the potential for the spread of tuberculosis in the world. Sverdlovsk oblast (SO) of Russian Federation is the region with high levels of HIV and tuberculosis (TB). The search for new methods of forecasting of the future epidemic situation for tuberculosis has become particularly relevant. The aim was to develop an effective method for predicting the epidemic situation of tuberculosis using an artificial intelligence (AI) method in the format of a dynamic simulation model based on AI technologies. Statistical data was loaded from the state statistical reporting on TB patients for the period 2007-2017. The parameters were controlled through a system of inequalities. The proposed SDM made it possible to identify and reliably calculate trends of TB epidemiological indicators. Comparison of the predicted values made in 2017 with the actual values of 2018-2021 revealed a reliable coincidence of the trend of movement of TB epidemiological indicators in the region, the maximum deviation was no more than 14.82%. The forecast results obtained with SDM are quite suitable for practical use. Especially, in operational resource planning of measures to counteract the spread of tuberculosis at the regional level.


Subject(s)
Epidemics , HIV Infections , Tuberculosis , Humans , HIV Infections/epidemiology , Artificial Intelligence , Tuberculosis/epidemiology , Forecasting , Russia/epidemiology
20.
Infect Control Hosp Epidemiol ; 42(4): 388-391, 2021 04.
Article in English | MEDLINE | ID: covidwho-2096421

ABSTRACT

OBJECTIVE: Presenteeism is an expensive and challenging problem in the healthcare industry. In anticipation of the staffing challenges expected with the COVID-19 pandemic, we examined a decade of payroll data for a healthcare workforce. We aimed to determine the effect of seasonal influenza-like illness (ILI) on absences to support COVID-19 staffing plans. DESIGN: Retrospective cohort study. SETTING: Large academic medical center in the United States. PARTICIPANTS: Employees of the academic medical center who were on payroll between the years of 2009 and 2019. METHODS: Biweekly institutional payroll data was evaluated for unscheduled absences as a marker for acute illness-related work absences. Linear regression models, stratified by payroll status (salaried vs hourly employees) were developed for unscheduled absences as a function of local ILI. RESULTS: Both hours worked and unscheduled absences were significantly related to the community prevalence of influenza-like illness in our cohort. These effects were stronger in hourly employees. CONCLUSIONS: Organizations should target their messaging at encouraging salaried staff to stay home when ill.


Subject(s)
Absenteeism , COVID-19/epidemiology , Health Personnel/statistics & numerical data , Presenteeism/statistics & numerical data , Workforce , Academic Medical Centers/organization & administration , Academic Medical Centers/statistics & numerical data , Epidemics , Health Personnel/psychology , Humans , Minnesota/epidemiology , Retrospective Studies
SELECTION OF CITATIONS
SEARCH DETAIL