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1.
Trop Med Infect Dis ; 7(10)2022 Oct 07.
Article in English | MEDLINE | ID: covidwho-2066499

ABSTRACT

Contrary to expectation, dengue incidence decreased in many countries during the period when stringent population movement restrictions were imposed to combat COVID-19. Using a seasonal autoregressive integrated moving average model, we previously reported a 74% reduction in the predicted number of dengue cases from March 2020 to April 2021 in the whole of Sri Lanka, with reductions occurring in all 25 districts in the country. The reduction in dengue incidence was accompanied by an 87% reduction in larval collections of Aedes vectors in the northern city of Jaffna. It was proposed that movement restrictions led to reduced human contact and blood feeding by Aedes vectors, accompanied by decreased oviposition and vector densities, which were responsible for diminished dengue transmission. These findings are extended in the present study by investigating the relationship between dengue incidence, population movement restrictions, and vector larval collections between May 2021 and July 2022, when movement restrictions began to be lifted, with their complete removal in November 2021. The new findings further support our previous proposal that population movement restrictions imposed during the COVID-19 pandemic reduced dengue transmission primarily by influencing human-Aedes vector interaction dynamics.

2.
Comput Methods Appl Mech Eng ; 401: 115541, 2022 Nov 01.
Article in English | MEDLINE | ID: covidwho-2031208

ABSTRACT

The outbreak of COVID-19, beginning in 2019 and continuing through the time of writing, has led to renewed interest in the mathematical modeling of infectious disease. Recent works have focused on partial differential equation (PDE) models, particularly reaction-diffusion models, able to describe the progression of an epidemic in both space and time. These studies have shown generally promising results in describing and predicting COVID-19 progression. However, people often travel long distances in short periods of time, leading to nonlocal transmission of the disease. Such contagion dynamics are not well-represented by diffusion alone. In contrast, ordinary differential equation (ODE) models may easily account for this behavior by considering disparate regions as nodes in a network, with the edges defining nonlocal transmission. In this work, we attempt to combine these modeling paradigms via the introduction of a network structure within a reaction-diffusion PDE system. This is achieved through the definition of a population-transfer operator, which couples disjoint and potentially distant geographic regions, facilitating nonlocal population movement between them. We provide analytical results demonstrating that this operator does not disrupt the physical consistency or mathematical well-posedness of the system, and verify these results through numerical experiments. We then use this technique to simulate the COVID-19 epidemic in the Brazilian region of Rio de Janeiro, showcasing its ability to capture important nonlocal behaviors, while maintaining the advantages of a reaction-diffusion model for describing local dynamics.

3.
Travel Med Infect Dis ; 49: 102357, 2022.
Article in English | MEDLINE | ID: covidwho-2016100

ABSTRACT

BACKGROUND: China is beginning to transform from a migrant exporting country to a migrant importing country. Our study aimed to assess risks of imported tuberculosis among travellers and to determine risk factors, to tailor institutional guidelines. METHODS: We conducted an observational, retrospective, population-based cohort study. Molecular epidemiology surveillance methods were used to screen travellers for cases of pulmonary tuberculosis (PTB) at Guangzhou Port in China from January 2010 to December 2016. RESULTS: A total of 165,369 travellers from 190 countries and regions were screened for PTB. The rate of suspected PTB, laboratory confirmed rate, and the total detection rate in emigrants were significantly higher than those in travellers (p<0.01). There were four differences in the PTB screening process between emigrants and travellers. According to the transmission risk degree of the tuberculosis, forty high-risk PTB importing countries were divided into five levels. The travellers diagnosed with PTB were significantly younger than the emigrants (p<0.01). The distribution of genotypes differed significantly between the travellers and emigrants (p<0.001). CONCLUSIONS: PTB screening process in travellers at ports should include a risk assessment of high-risk groups. It should reduce diagnosis time by rapid molecular detection methods and strengthen drug resistant (DR) transmission and monitoring of imported PTB strains through molecular genotyping at ports.


Subject(s)
Emigrants and Immigrants , Tuberculosis, Pulmonary , Tuberculosis , China/epidemiology , Cohort Studies , Humans , Retrospective Studies , Tuberculosis, Pulmonary/diagnosis , Tuberculosis, Pulmonary/epidemiology
4.
Comput Biol Med ; 149: 106046, 2022 10.
Article in English | MEDLINE | ID: covidwho-2003992

ABSTRACT

In this paper, we propose a coronavirus disease (COVID-19) epidemiological model called SEIR-FMi (Susceptible-Exposed-Infectious-Recovery with Flow and Medical investments) to study the effects of intra-city population movement, inter-city population movement, and medical resource investment on the spread of the COVID-19 epidemic. We theoretically derived the reproduction number of the SEIR-FMi model by using the next-generation matrix method and empirically simulate the individual impacts of population movement and medical resource investment on epidemic control. We found that intra- and inter-city population movements will increase the risk of epidemic spread, and the effect of inter-city population movement on low-risk areas is higher than that on high-risk areas. Increasing medical resource investment can not only speed up the recover rate of patients but also reduce the growth rate of infected cases and shorten the spread duration of the epidemic. We collected data on intra-city population movement, inter-city population movement, medical resource investment, and confirmed cases in the cities of Wuhan, Jingzhou, and Xiangyang, Hubei Province, China, from January 15 to March 15, 2020. Using the collected data, we validated that the proposed SEIR-FMi model performs well in simulating the spread of COVID-19 in the three cities. Meanwhile, this study confirms that three non-pharmaceutical interventions, namely community isolation, population mobility control, and medical resource aid, applied during the epidemic period are indispensable in controlling the spread of COVID-19 in the three cities.


Subject(s)
COVID-19 , COVID-19/epidemiology , China/epidemiology , Cities/epidemiology , Epidemiological Models , Humans , SARS-CoV-2
5.
Bull Math Biol ; 84(9): 94, 2022 08 01.
Article in English | MEDLINE | ID: covidwho-1966172

ABSTRACT

The coronavirus disease (COVID-19) has led to a global pandemic and caused huge healthy and economic losses. Non-pharmaceutical interventions, especially contact tracing and social distance restrictions, play a vital role in the control of COVID-19. Understanding the spatial impact is essential for designing such a control policy. Based on epidemic data of the confirmed cases after the Wuhan lockdown, we calculate the invasive reproduction numbers of COVID-19 in the different regions of China. Statistical analysis indicates a significant positive correlation between the reproduction numbers and the population input sizes from Wuhan, which indicates that the large-scale population movement contributed a lot to the geographic spread of COVID-19 in China. Moreover, there is a significant positive correlation between reproduction numbers and local population densities, which shows that the higher population density intensifies the spread of disease. Considering that in the early stage, there were sequential imported cases that affected the estimation of reproduction numbers, we classify the imported cases and local cases through the information of epidemiological data and calculate the net invasive reproduction number to quantify the local spread of the epidemic. The results are applied to the design of border control policy on the basis of vaccination coverage.


Subject(s)
COVID-19 , COVID-19/epidemiology , China/epidemiology , Communicable Disease Control/methods , Humans , Mathematical Concepts , Models, Biological , Population Density , SARS-CoV-2
6.
Math Biosci Eng ; 19(3): 3177-3201, 2022 01 20.
Article in English | MEDLINE | ID: covidwho-1662736

ABSTRACT

Patch models can better reflect the impact of spatial heterogeneity and population mobility on disease transmission. While, there is relatively little work on using patch models to study the role of travel restrictions, contact tracing and vaccination in COVID-19 epidemic. In this paper, based on COVID-19 epidemic propagation and diffusion mechanism, we establish a dynamic model of disease spread among two patches in which Wuhan is regarded as one patch and the rest of Mainland China (outside Wuhan) as the other patch. The existence of the final size is proved theoretically and some model parameters are estimated by using the reported confirmed cases. The results show that travel restrictions greatly reduce the number of confirmed cases in Mainland China, and the earlier enforced, the fewer confirmed cases. However, it is impossible to bring the COVID-19 epidemic under control and lift travel restrictions on April 8, 2020 by imposing travel restrictions alone, the same is true for contact tracing. While, the disease can always be controlled if the protection rate of herd immunity is high enough and the corresponding critical threshold is given. Therefore, in order to quickly control the spread of the emerging infectious disease (such as COVID-19), it is necessary to combine a variety of control measures and develop vaccines and therapeutic drugs as soon as possible.


Subject(s)
COVID-19 Vaccines , COVID-19 , Communicable Diseases, Emerging , Contact Tracing , Infection Control , Travel , COVID-19 Vaccines/administration & dosage , China/epidemiology , Communicable Diseases, Emerging/epidemiology , Communicable Diseases, Emerging/prevention & control , Humans , Infection Control/methods , SARS-CoV-2
7.
Ann Epidemiol ; 64: 76-82, 2021 12.
Article in English | MEDLINE | ID: covidwho-1401177

ABSTRACT

PURPOSE: Early COVID-19 mitigation relied on people staying home except for essential trips. The ability to stay home may differ by sociodemographic factors. We analyzed how factors related to social vulnerability impact a community's ability to stay home during a stay-at-home order. METHODS: Using generalized, linear mixed models stratified by stay-at-home order (mandatory or not mandatory), we analyzed county-level stay-at-home behavior (inferred from mobile devices) during a period when a majority of United States counties had stay-at-home orders (April 7-April 20, 2020) with the Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI). RESULTS: Counties with higher percentages of single-parent households, mobile homes, and persons with lower educational attainment were associated with lower stay-at-home behavior compared with counties with lower respective percentages. Counties with higher unemployment, higher percentages of limited-English-language speakers, and more multi-unit housing were associated with increases in stay-at-home behavior compared with counties with lower respective percentages. Stronger effects were found in counties with mandatory orders. CONCLUSIONS: Sociodemographic factors impact a community's ability to stay home during COVID-19 stay-at-home orders. Communities with higher social vulnerability may have more essential workers without work-from-home options or fewer resources to stay home for extended periods, which may increase risk for COVID-19. Results are useful for tailoring messaging, COVID-19 vaccine delivery, and public health responses to future outbreaks.


Subject(s)
COVID-19 , COVID-19 Vaccines , Humans , SARS-CoV-2 , United States
8.
Geohealth ; 5(5): e2021GH000402, 2021 May.
Article in English | MEDLINE | ID: covidwho-1240768

ABSTRACT

The ongoing Coronavirus Disease 2019 (COVID-19) has posed a serious threat to human public health and global economy. Population mobility is an important factor that drives the spread of COVID-19. This study aimed to quantitatively evaluate the impact of population flow on the spread of COVID-19 from a spatiotemporal perspective. To this end, a case study was carried out in Hubei Province, which was once the most affected area of COVID-19 outbreak in Mainland China. The geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal association between COVID-19 epidemic and population mobility. Two patterns of population flows, including the population inflow from Wuhan and intra-city population movement, were considered to construct explanatory variables. Results indicate that the GTWR model can reveal the spatial-temporal-varying relationships between COVID-19 and population mobility. Moreover, the association between COVID-19 case counts and population movements presented three stages of temporal variation characteristics due to the virus incubation period and implementation of strict lockdown measures. In the spatial dimension, evident geographical disparities were observed across Hubei Province. These findings can provide policymakers useful knowledge about the impact of population movement on the spatio-temporal transmission of COVID-19. Thus, targeted interventions, if necessary in certain time periods, can be implemented to restrict population flow in cities with high transmission risk.

9.
Clin Infect Dis ; 71(16): 2045-2051, 2020 11 19.
Article in English | MEDLINE | ID: covidwho-1153144

ABSTRACT

BACKGROUND: The unprecedented outbreak of corona virus disease 2019 (COVID-19) infection in Wuhan City has caused global concern; the outflow of the population from Wuhan was believed to be a main reason for the rapid and large-scale spread of the disease, so the government implemented a city-closure measure to prevent its transmission considering the large amount of travel before the Chinese New Year. METHODS: Based on the daily reported new cases and the population-movement data between 1 and 31 January, we examined the effects of population outflow from Wuhan on the geographical expansion of the infection in other provinces and cities of China, as well as the impacts of the city closure in Wuhan using different closing-date scenarios. RESULTS: We observed a significantly positive association between population movement and the number of the COVID-19 cases. The spatial distribution of cases per unit of outflow population indicated that the infection in some areas with a large outflow of population might have been underestimated, such as Henan and Hunan provinces. Further analysis revealed that if the city-closure policy had been implemented 2 days earlier, 1420 (95% confidence interval, 1059-1833) cases could have been prevented, and if 2 days later, 1462 (1090-1886) more cases would have been possible. CONCLUSIONS: Our findings suggest that population movement might be one important trigger for the transmission of COVID-19 infection in China, and the policy of city closure is effective in controlling the epidemic.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , China/epidemiology , Cities/epidemiology , Confidence Intervals , Humans , Pandemics
10.
Transbound Emerg Dis ; 69(2): 549-558, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1096940

ABSTRACT

Epicentres are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model. With this model, we revealed insight into how COVID-19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic.


Subject(s)
COVID-19 , Epidemics , Animals , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/veterinary , China/epidemiology , Cities , Epidemics/prevention & control , Models, Theoretical , Pandemics/prevention & control
11.
Rev. colomb. cardiol ; 27(3): 160-165, May-June 2020. graf
Article in Spanish | WHO COVID, LILACS (Americas) | ID: covidwho-718968

ABSTRACT

Resumen La pandemia por COVID-19 ha desencadenado un impacto tremendo en la humanidad debido a las implicaciones culturales, económicas y sociales que este tipo de infecciones ha ocasionado. Quizá preguntas iniciales, mirando en retrospectiva corta, acerca de los hechos que comenzaron esta infección, darían una luz para prevenir eventos de esta índole en el futuro. ¿Cuáles fueron las circunstancias que desencadenaron la pandemia?, ¿fue una serie de transmisiones zoonóticas que produjeron la infección en los humanos?, ¿ha tenido algo que ver el ambiente? El desafío de las grandes potencias para mantener la hegemonía, dará curso a una plétora de investigaciones acerca de la biología del virus, las variables epidemiológicas y las enfermedades crónicas de riesgo cardiovascular. En este manuscrito se analizan algunos de estos cuestionamientos.


Abstract The COVID-19 pandemic has had a tremendous impact on humanity due to the cultural, financial, and social implications caused by this type of infection. Perhaps some initial questions, looking at the short-term retrospective, about the facts that started this infection, could shed some light on preventing events of this kind in the future. What were the circumstances that triggered the pandemic? Was it a series of zoonotic transmissions that produced the infection in humans? Is it something to do with the environment? The challenge of the major powers to maintain dominance, will give rise to a plethora of studies on the biology of the virus, the epidemiological variables, and the chronic diseases of cardiovascular risk. Some of these questions are analysed in this article.


Subject(s)
Epidemiologic Factors , COVID-19 , Environment , Heart Disease Risk Factors
12.
Epidemiol Infect ; 148: e177, 2020 08 03.
Article in English | MEDLINE | ID: covidwho-693214

ABSTRACT

Increased population movements and increased mobility made it possible for severe acute respiratory syndrome coronavirus 2, which is mainly spread by respiratory droplets, to spread faster and more easily. This study tracked and analysed the development of the coronavirus 2019 (COVID-19) outbreak in the top 100 cities that were destinations for people who left Wuhan before the city entered lockdown. Data were collected from the top 100 destination cities for people who travelled from Wuhan before the lockdown, the proportion of people travelling into each city, the intensity of intracity travel and the daily reports of COVID-19. The proportion of the population that travelled from Wuhan to each city from 10 January 2020 to 24 January 2020, was positively correlated with and had a significant linear relationship with the cumulative number of confirmed cases of COVID-19 in each city after 24 January (all P < 0.01). After the State Council launched a multidepartment joint prevention and control effort on 22 January 2020 and compared with data collected on 18 February, the average intracity travel intensity of the aforementioned 100 cities decreased by 60-70% (all P < 0.001). The average intensity of intracity travel on the nth day in these cities during the development of the outbreak was positively related to the growth rate of the number of confirmed COVID-19 cases on the n + 5th day in these cities and had a significant linear relationship (P < 0.01). Higher intensities of population movement were associated with a higher incidence of COVID-19 during the pandemic. Restrictions on population movement can effectively curb the development of an outbreak.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Travel-Related Illness , Travel/statistics & numerical data , COVID-19 , China/epidemiology , Coronavirus Infections/transmission , Humans , Incidence , Pandemics , Pneumonia, Viral/transmission , Regression Analysis , SARS-CoV-2
14.
Soc Sci Med ; 255: 113036, 2020 06.
Article in English | MEDLINE | ID: covidwho-276255

ABSTRACT

This comment discusses the contribution of population movement to the spread of COVID-19, with a reference to the spread of SARS 17 years ago. We argue that the changing geography of migration, the diversification of jobs taken by migrants, the rapid growth of tourism and business trips, and the longer distance taken by people for family reunion are what make the spread of COVID-19 so differently from that of SARS. These changes in population movement are expected to continue. Hence, new strategies in disease prevention and control should be taken accordingly, which are also proposed in the comment.


Subject(s)
Coronavirus Infections/transmission , Human Migration/statistics & numerical data , Pneumonia, Viral/transmission , COVID-19 , China/epidemiology , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Humans , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Severe Acute Respiratory Syndrome/epidemiology , Severe Acute Respiratory Syndrome/transmission
15.
Emerg Microbes Infect ; 9(1): 988-990, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-102084

ABSTRACT

Since Dec 2019, China has experienced an outbreak caused by a novel coronavirus, 2019-nCoV. A travel ban was implemented for Wuhan, Hubei on Jan 23 to slow down the outbreak. We found a significant positive correlation between population influx from Wuhan and confirmed cases in other cities across China (R2 = 0.85, P < 0.001), especially cities in Hubei (R2 = 0.88, P < 0.001). Removing the travel restriction would have increased 118% (91%-172%) of the overall cases for the coming week, and a travel ban taken three days or a week earlier would have reduced 47% (26%-58%) and 83% (78%-89%) of the early cases. We would expect a 61% (48%-92%) increase of overall cumulative cases without any restrictions on returning residents, and 11% (8%-16%) increase if the travel ban stays in place for Hubei. Cities from Yangtze River Delta, Pearl River Delta, and Capital Economic Circle regions are at higher risk.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Travel/statistics & numerical data , Betacoronavirus/isolation & purification , COVID-19 , China/epidemiology , Coronavirus Infections/transmission , Humans , Pandemics , Pneumonia, Viral/transmission , SARS-CoV-2 , Travel/legislation & jurisprudence
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