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1.
PLoS One ; 17(2): e0263820, 2022.
Article in English | MEDLINE | ID: covidwho-1793524

ABSTRACT

Many factors play a role in outcomes of an emerging highly contagious disease such as COVID-19. Identification and better understanding of these factors are critical in planning and implementation of effective response strategies during such public health crises. The objective of this study is to examine the impact of factors related to social distancing, human mobility, enforcement strategies, hospital capacity, and testing capacity on COVID-19 outcomes within counties located in District of Columbia as well as the states of Maryland and Virginia. Longitudinal data have been used in the analysis to model county-level COVID-19 infection and mortality rates. These data include big location-based service data, which were collected from anonymized mobile devices and characterize various social distancing and human mobility measures within the study area during the pandemic. The results provide empirical evidence that lower rates of COVID-19 infection and mortality are linked with increased levels of social distancing and reduced levels of travel-particularly by public transit modes. Other preventive strategies and polices also prove to be influential in COVID-19 outcomes. Most notably, lower COVID-19 infection and mortality rates are linked with stricter enforcement policies and more severe penalties for violating stay-at-home orders. Further, policies that allow gradual relaxation of social distancing measures and travel restrictions as well as those requiring usage of a face mask are related to lower rates of COVID-19 infections and deaths. Additionally, increased access to ventilators and Intensive Care Unit (ICU) beds, which represent hospital capacity, are linked with lower COVID-19 mortality rates. On the other hand, gaps in testing capacity are related to higher rates of COVID-19 infection. The results also provide empirical evidence for reports suggesting that certain minority groups such as African Americans and Hispanics are disproportionately affected by the COVID-19 pandemic.


Subject(s)
Big Data , COVID-19/prevention & control , Physical Distancing , Public Health , Travel/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , District of Columbia/epidemiology , Female , Humans , Male , Maryland/epidemiology , Masks/statistics & numerical data , Middle Aged , Quarantine , SARS-CoV-2/isolation & purification , Virginia/epidemiology
2.
PLoS One ; 17(3): e0264682, 2022.
Article in English | MEDLINE | ID: covidwho-1724857

ABSTRACT

Global and local whole genome sequencing of SARS-CoV-2 enables the tracing of domestic and international transmissions. We sequenced Viral RNA from 37 sampled Covid-19 patients with RT-PCR-confirmed infections across the UAE and developed time-resolved phylogenies with 69 local and 3,894 global genome sequences. Furthermore, we investigated specific clades associated with the UAE cohort and, their global diversity, introduction events and inferred domestic and international virus transmissions between January and June 2020. The study comprehensively characterized the genomic aspects of the virus and its spread within the UAE and identified that the prevalence shift of the D614G mutation was due to the later introductions of the G-variant associated with international travel, rather than higher local transmissibility. For clades spanning different emirates, the most recent common ancestors pre-date domestic travel bans. In conclusion, we observe a steep and sustained decline of international transmissions immediately following the introduction of international travel restrictions.


Subject(s)
COVID-19/transmission , COVID-19/virology , Infection Control/methods , SARS-CoV-2/genetics , Travel/statistics & numerical data , Adolescent , Adult , Aged , COVID-19/epidemiology , Child , Child, Preschool , Female , Genome, Viral/genetics , Humans , Male , Middle Aged , Molecular Typing/methods , Mutation , Phylogeny , RNA, Viral , SARS-CoV-2/isolation & purification , Sequence Analysis, RNA , Travel-Related Illness , United Arab Emirates/epidemiology , Whole Genome Sequencing , Young Adult
3.
PLoS One ; 16(12): e0261381, 2021.
Article in English | MEDLINE | ID: covidwho-1638694

ABSTRACT

The Covid-19 pandemic has brought forth a major landscape shock in the mobility sector. Due to its recentness, researchers have just started studying and understanding the implications of this crisis on mobility. We contribute by combining mobility data from various sources to bring a novel angle to understanding mobility patterns during Covid-19. The goal is to expose relations between mobility and Covid-19 variables and understand them by using our data. This is crucial information for governments to understand and address the underlying root causes of the impact.


Subject(s)
COVID-19/economics , COVID-19/prevention & control , Marketing/statistics & numerical data , Pandemics/economics , Pandemics/prevention & control , Patient Isolation/methods , SARS-CoV-2 , Travel/statistics & numerical data , COVID-19/epidemiology , COVID-19/mortality , Humans , Netherlands/epidemiology
4.
Sci Rep ; 12(1): 699, 2022 01 13.
Article in English | MEDLINE | ID: covidwho-1621275

ABSTRACT

The global spread of the COVID-19 pandemic has followed complex pathways, largely attributed to the high virus infectivity, human travel patterns, and the implementation of multiple mitigation measures. The resulting geographic patterns describe the evolution of the epidemic and can indicate areas that are at risk of an outbreak. Here, we analyze the spatial correlations of new active cases in the USA at the county level and characterize the extent of these correlations at different times. We show that the epidemic did not progress uniformly and we identify various stages which are distinguished by significant differences in the correlation length. Our results indicate that the correlation length may be large even during periods when the number of cases declines. We find that correlations between urban centers were much more significant than between rural areas and this finding indicates that long-range spreading was mainly facilitated by travel between cities, especially at the first months of the epidemic. We also show the existence of a percolation transition in November 2020, when the largest part of the country was connected to a spanning cluster, and a smaller-scale transition in January 2021, with both times corresponding to the peak of the epidemic in the country.


Subject(s)
COVID-19/transmission , Cities/statistics & numerical data , Disease Outbreaks/statistics & numerical data , Geography/statistics & numerical data , Humans , Pandemics/statistics & numerical data , SARS-CoV-2/pathogenicity , Travel/statistics & numerical data , United States
5.
Sci Rep ; 12(1): 370, 2022 01 10.
Article in English | MEDLINE | ID: covidwho-1617000

ABSTRACT

COVID-19 outbreaks have had high mortality in low- and middle-income countries such as Ecuador. Human mobility is an important factor influencing the spread of diseases possibly leading to a high burden of disease at the country level. Drastic control measures, such as complete lockdown, are effective epidemic controls, yet in practice one hopes that a partial shutdown would suffice. It is an open problem to determine how much mobility can be allowed while controlling an outbreak. In this paper, we use statistical models to relate human mobility to the excess death in Ecuador while controlling for demographic factors. The mobility index provided by GRANDATA, based on mobile phone users, represents the change of number of out-of-home events with respect to a benchmark date (March 2nd, 2020). The study confirms the global trend that more men are dying than expected compared to women, and that people under 30 show less deaths than expected, particularly individuals younger than 20 with a death rate reduction between 22 and 27%. The weekly median mobility time series shows a sharp decrease in human mobility immediately after a national lockdown was declared on March 17, 2020 and a progressive increase towards the pre-lockdown level within two months. Relating median mobility to excess deaths shows a lag in its effect: first, a decrease in mobility in the previous two to three weeks decreases excess death and, more novel, we found an increase of mobility variability four weeks prior increases the number of excess deaths.


Subject(s)
COVID-19/mortality , Cause of Death , Communicable Disease Control/statistics & numerical data , Transportation/statistics & numerical data , Travel/statistics & numerical data , Adult , Algorithms , COVID-19/epidemiology , COVID-19/virology , Communicable Disease Control/methods , Ecuador/epidemiology , Female , Geography , Humans , Male , Pandemics/prevention & control , Population Dynamics , Risk Factors , SARS-CoV-2/physiology , Survival Rate , Time Factors , Young Adult
6.
PLoS One ; 16(12): e0261424, 2021.
Article in English | MEDLINE | ID: covidwho-1599330

ABSTRACT

The COVID-19 outbreak has caused two waves and spread to more than 90% of Canada's provinces since it was first reported more than a year ago. During the COVID-19 epidemic, Canadian provinces have implemented many Non-Pharmaceutical Interventions (NPIs). However, the spread of the COVID-19 epidemic continues due to the complex dynamics of human mobility. We develop a meta-population network model to study the transmission dynamics of COVID-19. The model takes into account the heterogeneity of mitigation strategies in different provinces of Canada, such as the timing of implementing NPIs, the human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences due to work and recreation. To determine which activity is most closely related to the dynamics of COVID-19, we use the cross-correlation analysis to find that the positive correlation is the highest between the mobility data of parks and the weekly number of confirmed COVID-19 from February 15 to December 13, 2020. The average effective reproduction numbers in nine Canadian provinces are all greater than one during the time period, and NPIs have little impact on the dynamics of COVID-19 epidemics in Ontario and Saskatchewan. After November 20, 2020, the average infection probability in Alberta became the highest since the start of the COVID-19 epidemic in Canada. We also observe that human activities around residences do not contribute much to the spread of the COVID-19 epidemic. The simulation results indicate that social distancing and constricting human mobility is effective in mitigating COVID-19 transmission in Canada. Our findings can provide guidance for public health authorities in projecting the effectiveness of future NPIs.


Subject(s)
COVID-19/prevention & control , COVID-19/transmission , Epidemics/prevention & control , SARS-CoV-2 , Travel/statistics & numerical data , Basic Reproduction Number/statistics & numerical data , COVID-19/epidemiology , Canada/epidemiology , Humans , Incidence , Models, Statistical , Physical Distancing , Quarantine/methods
7.
Sci Rep ; 11(1): 24171, 2021 12 17.
Article in English | MEDLINE | ID: covidwho-1593554

ABSTRACT

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.


Subject(s)
Algorithms , COVID-19/transmission , Cell Phone/statistics & numerical data , Hospitalization/statistics & numerical data , Models, Theoretical , Patient Admission/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , Disease Transmission, Infectious/statistics & numerical data , Forecasting/methods , Geography , Hospitalization/trends , Humans , Pandemics/prevention & control , Patient Admission/trends , Retrospective Studies , SARS-CoV-2/physiology , Sweden/epidemiology , Travel/statistics & numerical data
8.
PLoS One ; 16(3): e0243263, 2021.
Article in English | MEDLINE | ID: covidwho-1576004

ABSTRACT

As mobile device location data become increasingly available, new analyses are revealing the significant changes of mobility pattern when an unplanned event happened. With different control policies from local and state government, the COVID-19 outbreak has dramatically changed mobility behavior in affected cities. This study has been investigating the impact of COVID-19 on the number of people involved in crashes accounting for the intensity of different control measures using Negative Binomial (NB) method. Based on a comprehensive dataset of people involved in crashes aggregated in New York City during January 1, 2020 to May 24, 2020, people involved in crashes with respect to travel behavior, traffic characteristics and socio-demographic characteristics are found. The results show that the average person miles traveled on the main traffic mode per person per day, percentage of work trip have positive effect on person involved in crashes. On the contrary, unemployment rate and inflation rate have negative effects on person involved in crashes. Interestingly, different level of control policies during COVID-19 outbreak are closely associated with safety awareness, driving and travel behavior, and thus has an indirect influence on the frequency of crashes. Comparing to other three control policies including emergence declare, limits on mass gatherings, and ban on all nonessential gathering, the negative relationship between stay-at-home policy implemented in New York City from March 20, 2020 and the number of people involved crashes is found in our study.


Subject(s)
Accidents, Traffic/statistics & numerical data , Automobile Driving/statistics & numerical data , COVID-19 , Safety/statistics & numerical data , Travel/statistics & numerical data , Humans , New York City , Public Policy , Risk-Taking
10.
PLoS One ; 16(11): e0260269, 2021.
Article in English | MEDLINE | ID: covidwho-1526701

ABSTRACT

BACKGROUND: Feasibility of mobile Apps to monitor diseases has not been well documented particularly in developing countries. We developed and studied the feasibility of using a mobile App to collect daily data on COVID-19 symptoms and people's movements. METHODS: We used an open source software "KoBo Toolbox" to develop the App and installed it on low cost smart mobile phones. We named this App "Wetaase" ("protect yourself"). The App was tested on 30 selected households from 3 densely populated areas of Kampala, Uganda, and followed them for 3 months. One trained member per household captured the data in the App for each enrolled member and uploaded it to a virtual server on a daily basis. The App is embedded with an algorithm that flags participants who report fever and any other COVID-19 related symptom. RESULTS: A total of 101 participants were enrolled; 61% female; median age 23 (interquartile range (IQR): 17-36) years. Usage of the App was 78% (95% confidence interval (CI): 77.0%-78.8%). It increased from 40% on day 1 to a peak of 81% on day 45 and then declined to 59% on day 90. Usage of the App did not significantly vary by site, sex or age. Only 57/6617 (0.86%) records included a report of at least one of the 17 listed COVID-19 related symptoms. The most reported symptom was flu/runny nose (21%) followed by sneezing (15%), with the rest ranging between 2% and 7%. Reports on movements away from home were 45% with 74% going to markets or shops. The participants liked the "Wetaase" App and recommended it for use as an alert system for COVID-19. CONCLUSION: Usage of the "Wetaase" App was high (78%) and it was similar across the three study sites, sex and age groups. Reporting of symptoms related to COVID-19 was low. Movements were mainly to markets and shops. Users reported that the App was easy to use and recommended its scale up. We recommend that this App be assessed at a large scale for feasibility, usability and acceptability as an additional tool for increasing alerts on COVID-19 in Uganda and similar settings.


Subject(s)
COVID-19/diagnosis , Contact Tracing/methods , Mobile Applications , Telemedicine/methods , Adolescent , Adult , Body Temperature , COVID-19/epidemiology , COVID-19/prevention & control , Feasibility Studies , Female , Humans , Male , Sensitivity and Specificity , Telemedicine/standards , Travel/statistics & numerical data , Uganda
11.
PLoS Comput Biol ; 17(10): e1009473, 2021 10.
Article in English | MEDLINE | ID: covidwho-1496327

ABSTRACT

Infectious diseases attack humans from time to time and threaten the lives and survival of people all around the world. An important strategy to prevent the spatial spread of infectious diseases is to restrict population travel. With the reduction of the epidemic situation, when and where travel restrictions can be lifted, and how to organize orderly movement patterns become critical and fall within the scope of this study. We define a novel diffusion distance derived from the estimated mobility network, based on which we provide a general model to describe the spatiotemporal spread of infectious diseases with a random diffusion process and a deterministic drift process of the population. We consequently develop a multi-source data fusion method to determine the population flow in epidemic areas. In this method, we first select available subregions in epidemic areas, and then provide solutions to initiate new travel flux among these subregions. To verify our model and method, we analyze the multi-source data from mainland China and obtain a new travel flux triggering scheme in the selected 29 cities with the most active population movements in mainland China. The testable predictions in these selected cities show that reopening the borders in accordance with our proposed travel flux will not cause a second outbreak of COVID-19 in these cities. The finding provides a methodology of re-triggering travel flux during the weakening spread stage of the epidemic.


Subject(s)
COVID-19/epidemiology , Epidemics , SARS-CoV-2 , Travel , COVID-19/prevention & control , COVID-19/transmission , China/epidemiology , Cities , Computational Biology , Humans , Mathematical Concepts , Models, Biological , Spatio-Temporal Analysis , Travel/statistics & numerical data
12.
Nat Commun ; 12(1): 5769, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1447305

ABSTRACT

Distinct SARS-CoV-2 lineages, discovered through various genomic surveillance initiatives, have emerged during the pandemic following unprecedented reductions in worldwide human mobility. We here describe a SARS-CoV-2 lineage - designated B.1.620 - discovered in Lithuania and carrying many mutations and deletions in the spike protein shared with widespread variants of concern (VOCs), including E484K, S477N and deletions HV69Δ, Y144Δ, and LLA241/243Δ. As well as documenting the suite of mutations this lineage carries, we also describe its potential to be resistant to neutralising antibodies, accompanying travel histories for a subset of European cases, evidence of local B.1.620 transmission in Europe with a focus on Lithuania, and significance of its prevalence in Central Africa owing to recent genome sequencing efforts there. We make a case for its likely Central African origin using advanced phylogeographic inference methodologies incorporating recorded travel histories of infected travellers.


Subject(s)
COVID-19/transmission , COVID-19/virology , SARS-CoV-2/genetics , Africa, Central/epidemiology , Antibodies, Neutralizing/immunology , COVID-19/epidemiology , Europe/epidemiology , Humans , Immune Evasion/genetics , Mutation , Phylogeny , Phylogeography , SARS-CoV-2/classification , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/genetics , Travel/statistics & numerical data
13.
Infect Genet Evol ; 95: 105087, 2021 11.
Article in English | MEDLINE | ID: covidwho-1442480

ABSTRACT

The novel coronavirus SARS-CoV-2 was first detected in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced a variety of social distancing interventions to slow the virus spread, that had different degrees of success. Understanding the role of non-pharmaceutical interventions (NPIs) on COVID-19 transmission in different settings is highly important. While most such studies have focused on China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. The aim of this epidemiological study is to fill this gap by analyzing the characteristics of the first months of the epidemic in Ukraine using agent-based modelling and phylodynamics. Specifically, first we studied the dynamics of COVID-19 incidence and mortality and explored the impact of epidemic NPIs. Our stochastic model suggests, that even a small delay of weeks could have increased the number of cases by up to 50%, with the potential to overwhelm hospital systems. Second, the genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic. Our findings support the conclusion that the implemented travel restrictions may have had limited impact on the epidemic spread. Third, the basic reproduction number for the epidemic that has been estimated independently from case counts data and from genomic data suggest sustained intra-country transmissions.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Genome, Viral , Models, Statistical , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , COVID-19/virology , China/epidemiology , Epidemiological Monitoring , Europe/epidemiology , Humans , Incidence , North America/epidemiology , Phylogeny , Physical Distancing , SARS-CoV-2/classification , SARS-CoV-2/isolation & purification , Travel/statistics & numerical data , Ukraine/epidemiology
14.
Clin Microbiol Infect ; 26(10): 1380-1385, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-1439953

ABSTRACT

OBJECTIVES: The aim was to determine the clinical characteristics of COVID-19 patients because the SARS-CoV-2 virus continues to circulate in the population. METHODS: This is a retrospective, multicentre, cohort study. Adult COVID-19 cases from four hospitals in Zhejiang were enrolled and clustered into three groups based on epidemiological history. First-generation patients had a travel history to Hubei within 14 days before disease onset; second-generation patients had a contact history with first-generation patients; third-generation patients had a contact history with second-generation patients. Demographic, clinical characteristics, clinical outcomes and duration of viral shedding were analysed. RESULTS: A total of 171 patients were enrolled, with 83, 44 and 44 patients in the first-, second-, and third-generation, respectively. Compared with the first and second generations, third-generation patients were older (61.3 vs. 48.3 and 44.0 years, p < 0.001) and had more coexisting conditions (56.8% vs. 36.1% and 27.3%, p 0.013). At 7 ± 1 days from illness onset, third-generation patients had lower lymphocyte (0.6 vs. 0.8 and 0.8 × 109/L, p 0.007), higher C-reactive protein (29.7 vs. 17.1 and 13.8 mg/L, p 0.018) and D-dimer (1066 vs. 412.5 and 549 µg/L, p 0.002) and more lesions involving the pulmonary lobes (lobes ≥5, 81.8% vs. 53.0% and 34.1%, p < 0.001). The proportions of third-generation patients developing severe illness (72.7% vs. 32.5% and 27.3%, p < 0.001), critical illness (38.6% vs. 10.8% and 6.8%, p < 0.001) and receiving endotracheal intubation (20.5% vs. 3.6% and 2.3%, p 0.002) were higher than in the other two groups. DISCUSSION: Third-generation patients were older, had more underlying comorbidities and had a higher proportion of severe or critical illness than first- and second-generation patients.


Subject(s)
Betacoronavirus/pathogenicity , Coronavirus Infections/epidemiology , Diabetes Mellitus/epidemiology , Hypertension/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Pulmonary Disease, Chronic Obstructive/epidemiology , Adult , Biomarkers/blood , C-Reactive Protein/metabolism , COVID-19 , China/epidemiology , Comorbidity , Contact Tracing , Coronavirus Infections/blood , Coronavirus Infections/physiopathology , Coronavirus Infections/transmission , Diabetes Mellitus/blood , Diabetes Mellitus/physiopathology , Female , Fibrin Fibrinogen Degradation Products/metabolism , Humans , Hypertension/blood , Hypertension/physiopathology , Interleukin-6/blood , Intubation, Intratracheal , Lymphocytes/pathology , Lymphocytes/virology , Male , Middle Aged , Pneumonia, Viral/blood , Pneumonia, Viral/physiopathology , Pneumonia, Viral/transmission , Pulmonary Disease, Chronic Obstructive/blood , Pulmonary Disease, Chronic Obstructive/physiopathology , Retrospective Studies , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Travel/statistics & numerical data , Virus Shedding
15.
Sci Rep ; 11(1): 18951, 2021 09 23.
Article in English | MEDLINE | ID: covidwho-1437686

ABSTRACT

A spatial susceptible-exposed-infectious-recovered (SEIR) model is developed to analyze the effects of restricting interregional mobility on the spatial spread of the coronavirus disease 2019 (COVID-19) infection in Japan. National and local governments have requested that residents refrain from traveling between prefectures during the state of emergency. However, the extent to which restricting interregional mobility prevents infection expansion is unclear. The spatial SEIR model describes the spatial spread pattern of COVID-19 infection when people commute or travel to a prefecture in the daytime and return to their residential prefecture at night. It is assumed that people are exposed to an infection risk during their daytime activities. The spatial spread of COVID-19 infection is simulated by integrating interregional mobility data. According to the simulation results, interregional mobility restrictions can prevent the geographical expansion of the infection. On the other hand, in urban prefectures with many infectious individuals, residents are exposed to higher infection risk when their interregional mobility is restricted. The simulation results also show that interregional mobility restrictions play a limited role in reducing the total number of infected individuals in Japan, suggesting that other non-pharmaceutical interventions should be implemented to reduce the epidemic size.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , Disease Susceptibility/epidemiology , Epidemics , Humans , Japan/epidemiology , Models, Theoretical , SARS-CoV-2/pathogenicity , Transportation/statistics & numerical data , Travel/statistics & numerical data , Travel/trends
16.
Elife ; 102021 09 17.
Article in English | MEDLINE | ID: covidwho-1438866

ABSTRACT

Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.


Subject(s)
Human Migration/statistics & numerical data , Models, Biological , Rural Population/statistics & numerical data , Africa South of the Sahara/epidemiology , Humans , Spatial Analysis , Travel/statistics & numerical data
17.
Parasit Vectors ; 14(1): 483, 2021 Sep 19.
Article in English | MEDLINE | ID: covidwho-1430472

ABSTRACT

BACKGROUND: During the period of the coronavirus disease 2019 (COVID-19) outbreak, strong intervention measures, such as lockdown, travel restriction, and suspension of work and production, may have curbed the spread of other infectious diseases, including natural focal diseases. In this study, we aimed to study the impact of COVID-19 prevention and control measures on the reported incidence of natural focal diseases (brucellosis, malaria, hemorrhagic fever with renal syndrome [HFRS], dengue, severe fever with thrombocytopenia syndrome [SFTS], rabies, tsutsugamushi and Japanese encephalitis [JE]). METHODS: The data on daily COVID-19 confirmed cases and natural focal disease cases were collected from Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Provincial CDC). We described and compared the difference between the incidence in 2020 and the incidence in 2015-2019 in four aspects: trend in reported incidence, age, sex, and urban and rural distribution. An autoregressive integrated moving average (ARIMA) (p, d, q) × (P, D, Q)s model was adopted for natural focal diseases, malaria and severe fever with thrombocytopenia syndrome (SFTS), and an ARIMA (p, d, q) model was adopted for dengue. Nonparametric tests were used to compare the reported and the predicted incidence in 2020, the incidence in 2020 and the previous 4 years, and the difference between the duration from illness onset date to diagnosed date (DID) in 2020 and in the previous 4 years. The determination coefficient (R2) was used to evaluate the goodness of fit of the model simulation. RESULTS: Natural focal diseases in Jiangsu Province showed a long-term seasonal trend. The reported incidence of natural focal diseases, malaria and dengue in 2020 was lower than the predicted incidence, and the difference was statistically significant (P < 0.05). The reported incidence of brucellosis in July, August, October and November 2020, and SFTS in May to November 2020 was higher than that in the same period in the previous 4 years (P < 0.05). The reported incidence of malaria in April to December 2020, HFRS in March, May and December 2020, and dengue in July to November 2020 was lower than that in the same period in the previous 4 years (P < 0.05). In males, the reported incidence of malaria in 2020 was lower than that in the previous 4 years, and the reported incidence of dengue in 2020 was lower than that in 2017-2019. The reported incidence of malaria in the 20-60-year age group was lower than that in the previous 4 years; the reported incidence of dengue in the 40-60-year age group was lower than that in 2016-2018. The reported cases of malaria in both urban and rural areas were lower than in the previous 4 years. The DID of brucellosis and SFTS in 2020 was shorter than that in 2015-2018; the DID of tsutsugamushi in 2020 was shorter than that in the previous 4 years. CONCLUSIONS: Interventions for COVID-19 may help control the epidemics of natural focal diseases in Jiangsu Province. The reported incidence of natural focal diseases, especially malaria and dengue, decreased during the outbreak of COVID-19 in 2020. COVID-19 prevention and control measures had the greatest impact on the reported incidence of natural focal diseases in males and people in the 20-60-year age group.


Subject(s)
Brucellosis/epidemiology , COVID-19/prevention & control , Dengue/epidemiology , Malaria/epidemiology , Adult , Age Distribution , Aged , COVID-19/epidemiology , China/epidemiology , Disease Outbreaks , Female , Humans , Incidence , Male , Middle Aged , Physical Distancing , Severe Fever with Thrombocytopenia Syndrome/epidemiology , Travel/statistics & numerical data , Young Adult
18.
Nat Microbiol ; 6(10): 1271-1278, 2021 10.
Article in English | MEDLINE | ID: covidwho-1402078

ABSTRACT

Genomics, combined with population mobility data, used to map importation and spatial spread of SARS-CoV-2 in high-income countries has enabled the implementation of local control measures. Here, to track the spread of SARS-CoV-2 lineages in Bangladesh at the national level, we analysed outbreak trajectory and variant emergence using genomics, Facebook 'Data for Good' and data from three mobile phone operators. We sequenced the complete genomes of 67 SARS-CoV-2 samples (collected by the IEDCR in Bangladesh between March and July 2020) and combined these data with 324 publicly available Global Initiative on Sharing All Influenza Data (GISAID) SARS-CoV-2 genomes from Bangladesh at that time. We found that most (85%) of the sequenced isolates were Pango lineage B.1.1.25 (58%), B.1.1 (19%) or B.1.36 (8%) in early-mid 2020. Bayesian time-scaled phylogenetic analysis predicted that SARS-CoV-2 first emerged during mid-February in Bangladesh, from abroad, with the first case of coronavirus disease 2019 (COVID-19) reported on 8 March 2020. At the end of March 2020, three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity in Bangladesh, combined with the mobility data, revealed that the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka (the capital of Bangladesh) and the rest of the country, disseminated three dominant viral lineages. Further analysis of an additional 85 genomes (November 2020 to April 2021) found that importation of variant of concern Beta (B.1.351) had occurred and that Beta had become dominant in Dhaka. Our interpretation that population mobility out of Dhaka, and travel from urban hotspots to rural areas, disseminated lineages in Bangladesh in the first wave continues to inform government policies to control national case numbers by limiting within-country travel.


Subject(s)
COVID-19/transmission , Cell Phone/statistics & numerical data , Genome, Viral/genetics , SARS-CoV-2/genetics , Social Media/statistics & numerical data , Bangladesh/epidemiology , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , Genomics , Health Policy/legislation & jurisprudence , Humans , Phylogeny , Population Dynamics/statistics & numerical data , SARS-CoV-2/classification , Travel/legislation & jurisprudence , Travel/statistics & numerical data
19.
Infect Genet Evol ; 95: 105057, 2021 11.
Article in English | MEDLINE | ID: covidwho-1392456

ABSTRACT

The COVID-19 pandemic induced by the SARS-CoV-2 virus and its variants has ravaged most countries around the world including Bangladesh. We have analyzed publicly available genomic data to understand the current COVID-19 outbreak scenario as well as the evolutionary origin and transmission routes of SARS-CoV-2 isolates in Bangladesh. All the early isolates as well as recent B.1.1.7 and B.1.351 variants had already spread across the major divisional cities of Bangladesh. A sex biasness towards male COVID-19 patient samples sequencing has been observed over female patient samples in all age-group, that could be the trend in infection rate. Phylogenetic analysis indicated a total of 13 estimated countries, including Italy, India, United Kingdom, Saudi Arabia, United Arab Emirates, Germany, Australia, New Zealand, South Africa, Democratic Republic of the Congo, United States, Russia, and Denmark, could be the possible origin introduced SARS-CoV-2 isolates in Bangladesh because of regional and intercontinental travel. Recent, B.1.1.7 variant could be imported from a total of 7 estimated countries including UK, India, Nigeria, Spain, Ireland, Australia, and Indonesia, while South Africa and the United States are the most likely sources of B.1.351 variant in Bangladesh. Based on these findings, public health strategies could be designed and implemented to reduce the local transmission of the virus.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Genome, Viral , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Adolescent , Adult , Africa/epidemiology , Aged , Aged, 80 and over , Australia/epidemiology , Bangladesh/epidemiology , COVID-19/virology , Child , Epidemiological Monitoring , Europe/epidemiology , Female , Humans , Male , Middle Aged , Molecular Epidemiology , Phylogeny , SARS-CoV-2/classification , SARS-CoV-2/isolation & purification , Sequence Analysis, RNA , Travel/statistics & numerical data , United States/epidemiology
20.
Sci Rep ; 11(1): 17328, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1379336

ABSTRACT

Public health officials discouraged travel and non-household gatherings for Thanksgiving, but data suggests that travel increased over the holidays. The objective of this analysis was to assess associations between holiday gatherings and SARS-CoV-2 positivity in the weeks following Thanksgiving. Using an online survey, we sampled 7770 individuals across 10 US states from December 4-18, 2020, about 8-22 days post-Thanksgiving. Participants were asked about Thanksgiving, COVID-19 symptoms, and SARS-CoV-2 testing and positivity in the prior 2 weeks. Logistic regression was used to identify factors associated with SARS-CoV-2 positivity and COVID-19 symptoms in the weeks following Thanksgiving. An activity score measured the total number of non-essential activities an individual participated in the prior 2 weeks. The probability of community transmission was estimated using Markov Chain Monte Carlo (MCMC) methods. While 47.2% had Thanksgiving at home with household members, 26.9% had guests and 25.9% traveled. There was a statistically significant interaction between how people spent Thanksgiving, the frequency of activities, and SARS-CoV-2 test positivity in the prior 2 weeks (p < 0.05). Those who had guests for Thanksgiving or traveled were only more likely to test positive for SARS-CoV-2 if they also had high activity (e.g., participated in > one non-essential activity/day in the prior 2 weeks). Had individuals limited the number and frequency of activities post-Thanksgiving, cases in surveyed individuals would be reduced by > 50%. As travel continues to increase and the more contagious Delta variant starts to dominate transmission, it is critical to promote how to gather in a "low-risk" manner (e.g., minimize other non-essential activities) to mitigate the need for nationwide shelter-at-home orders.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Travel/statistics & numerical data , Adult , COVID-19 Testing , Female , Holidays , Humans , Male , Markov Chains , Middle Aged , Monte Carlo Method , Public Health , United States/epidemiology
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