scholarly journals The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

2020 ◽  
Vol 148 ◽  
Author(s):  
Ezra Gayawan ◽  
Olushina O. Awe ◽  
Bamidele M. Oseni ◽  
Ikemefuna C. Uzochukwu ◽  
Adeshina Adekunle ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Virus Disease ◽  
Poisson Model ◽  
The Novel ◽  
Scarce Resources ◽  
Epidemic Dynamics ◽  
Disease Patterns ◽  
High Incidence ◽  
Spatio Temporal ◽  
The City

Abstract Corona virus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first detected in the city of Wuhan, China in December 2019. Although, the disease appeared in Africa later than other regions, it has now spread to virtually all countries on the continent. We provide early spatio-temporal dynamics of COVID-19 within the first 62 days of the disease's appearance on the African continent. We used a two-parameter hurdle Poisson model to simultaneously analyse the zero counts and the frequency of occurrence. We investigate the effects of important healthcare capacities including hospital beds and number of medical doctors in different countries. The results show that cases of the pandemic vary geographically across Africa with notably high incidence in neighbouring countries particularly in West and North Africa. The burden of the disease (per 100 000) mostly impacted Djibouti, Tunisia, Morocco and Algeria. Temporally, during the first 4 weeks, the burden was highest in Senegal, Egypt and Mauritania, but by mid-April it shifted to Somalia, Chad, Guinea, Tanzania, Gabon, Sudan and Zimbabwe. Currently, Namibia, Angola, South Sudan, Burundi and Uganda have the least burden. These findings could be useful in guiding epidemiological interventions and the allocation of scarce resources based on heterogeneity of the disease patterns.

scholarly journals The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

2020 ◽  
Author(s):  
Ezra Gayawan ◽  
Olawale Awe ◽  
Bamidele M Oseni ◽  
Ikemefuna C. Uzochukwu ◽  
Adeshina Adekunle ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Poisson Model ◽  
The Novel ◽  
African Continent ◽  
Scarce Resources ◽  
Epidemic Dynamics ◽  
Disease Patterns ◽  
Spatio Temporal ◽  
Novel Coronavirus ◽  
The City

AbstractThe novel coronavirus (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in the city of Wuhan, China in December 2019. Although, the disease appears on the African continent late, it has spread to virtually all the countries. We provide early spatio-temporal dynamics of COVID-19 within the first 62 days of the disease’s appearance on the African continent. We used a two-parameter hurdle Poisson model to simultaneously analyze the zero counts and the frequency of occurrence. We investigate the effects of important healthcare capacities including hospital beds and number of medical doctors in the different countries. The results show that cases of the pandemic vary geographically across Africa with notable high incidence in neighboring countries particularly in West and North Africa. The burden of the disease (per 100,000) was most felt in Djibouti Tunisia, Morocco and Algeria. Temporally, during the first 4 weeks, the burden was highest in Senegal, Egypt and Mauritania, but by mid-April it shifted to Somalia, Chad, Guinea, Tanzania, Gabon, Sudan, and Zimbabwe. Currently, Namibia, Angola, South Sudan, Burundi and Uganda have the least burden. The findings could be useful in implementing epidemiological intervention and allocation of scarce resources based on heterogeneity of the disease patterns.

scholarly journals Determinants of SARS-CoV-2 transmission to guide vaccination strategy in a city

2020 ◽  
Author(s):  
Sarah C. Brüningk ◽  
Juliane Klatt ◽  
Madlen Stange ◽  
Alfredo Mari ◽  
Myrta Brunner ◽  
...  
Keyword(s):  
Vaccination Strategy ◽  
Population Level ◽  
Data Sets ◽  
Epidemic Dynamics ◽  
Modelling Analysis ◽  
City Population ◽  
Senior Population ◽  
Spatio Temporal ◽  
Socioeconomic Data ◽  
The City

Transmission chains within cities provide an important contribution to case burden and economic impact during the ongoing COVID-19 pandemic, and should be a major focus for preventive measures to achieve containment. Here, at very high spatio-temporal resolution, we analysed determinants of SARS-CoV-2 transmission in a medium-sized European city. We combined detailed epidemiological, mobility, and socioeconomic data-sets with whole genome sequencing during the first SARS-CoV-2 wave. Both phylogenetic clustering and compartmental modelling analysis were performed based on the dominating viral variant (B.1-C15324T; 60% of all cases). Here we show that transmissions on the city population level are driven by the socioeconomically weaker and highly mobile groups. Simulated vaccination scenarios showed that vaccination of a third of the population at 90% efficacy prioritising the latter groups would induce a stronger preventive effect compared to vaccinating exclusively senior population groups first. Our analysis accounts for both social interaction and mobility on the basis of molecularly related cases, thereby providing high confidence estimates of the underlying epidemic dynamics that may readily be translatable to other municipal areas.

scholarly journals Evaluating Temporal Approximation Methods Using Burglary Data

2020 ◽  
Vol 9 (6) ◽  
pp. 386
Author(s):  
Lukas Oswald ◽  
Michael Leitner
Keyword(s):  
Temporal Analysis ◽  
Approximation Methods ◽  
The Novel ◽  
Occurrence Time ◽  
Criminal Intelligence ◽  
Time Stamps ◽  
Spatio Temporal ◽  
Crime Types ◽  
The City ◽  
Intelligence Service

Law enforcement is very interested in knowing when a crime has happened. Unfortunately, the occurrence time of a crime is often not exactly known. In such circumstances, estimating the most likely time that a crime has happened is crucial for spatio-temporal analysis. The main purpose of this research is to introduce two novel temporal approximation methods, termed retrospective temporal analysis (RTA) and extended retrospective temporal analysis (RTAext). Both methods are compared to six existing temporal approximation methods and subsequently evaluated in order to identify the method that can most accurately estimate the occurrence time of crimes. This research is conducted with 100,000+ burglary crimes from the city of Vienna, Austria provided by the Criminal Intelligence Service Austria, from 2009–2015. The RTA method assumes that crimes in the immediate past occur at very similar times as in the present and in the future. Historical crimes with accurately known time stamps can therefore be applied to estimate when crimes occur in the present/future. The RTAext method enhances one existing temporal approximation method, aoristicext, with probability values derived from historical crime data with accurately known time stamps. The results show that the RTA method performs superiorly to all other temporal approximation methods, including the novel RTAext method, in two out of the three crime types analyzed. Additionally, the RTAext method shows very good results that are similar to the best performing existing approximation methods.

scholarly journals Periodic synchronization of dengue epidemics in Thailand: the roles played by temperature and immunity

2021 ◽  
Author(s):  
Bernardo García-Carreras ◽  
Bingyi Yang ◽  
Mary K Grabowski ◽  
Lawrence W Sheppard ◽  
Angkana T Huang ◽  
...  
Keyword(s):  
Climate Change ◽  
Spatial Distribution ◽  
Seasonal Variation ◽  
Temporal Dynamics ◽  
Large Impact ◽  
Haemorrhagic Fever ◽  
Disease Patterns ◽  
Spatio Temporal ◽  
Dengue Epidemics ◽  
Impacts Of Climate Change

The spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, the largest impacts of climate change on dengue might be in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatio-temporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last thirty years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronization events. We show that patterns in synchrony of dengue are consistent with those observed in temperature. Applying a temperature-driven dengue model, we explore how dynamics of immunity interact with temperature to produce the observed multiannual dynamics and patterns in synchrony. While multiannual oscillations are readily produced by immunity in absence of multiannual timescales in temperature, synchrony in temperature can synchronise dengue dynamics in different locations. However, at higher mean temperatures and lower seasonal variation, immune dynamics become more predominant, and dengue dynamics become more insensitive to multiannual fluctuations in temperature. These findings can help underpin predictions of disease patterns as global temperatures rise.

scholarly journals Spatio-temporal cluster detection of chickenpox in Valencia, Spain in the period 2008-2012

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Adina Iftimi ◽  
Francisco Martínez-Ruiz ◽  
Ana Míguez Santiyán ◽  
Francisco Montes
Keyword(s):  
Time Frame ◽  
Spatiotemporal Pattern ◽  
Varicella Zoster ◽  
Temporal Cluster ◽  
High Incidence ◽  
Permutation Model ◽  
Spatio Temporal ◽  
Temporal Interaction ◽  
Airborne Disease ◽  
The City

Chickenpox is a highly contagious airborne disease caused by <em>Varicella zoster</em>, which affects nearly all non-immune children worldwide with an annual incidence estimated at 80-90 million cases. To analyze the spatiotemporal pattern of the chickenpox incidence in the city of Valencia, Spain two complementary statistical approaches were used. First, we evaluated the existence of clusters and spatio-temporal interaction; secondly, we used this information to find the locations of the spatio-temporal clusters via the space-time permutation model. The first method used detects any aggregation in our data but does not provide the spatial and temporal information. The second method gives the locations, areas and time-frame for the spatio-temporal clusters. An overall decreasing time trend, a pronounced 12-monthly periodicity and two complementary periods were observed. Several areas with high incidence, surrounding the center of the city were identified. The existence of aggregation in time and space was observed, and a number of spatio-temporal clusters were located.

scholarly journals Estimating the effect of social inequalities in the mitigation of COVID-19 across communities in Santiago de Chile

2020 ◽  
Author(s):  
Nicolò Gozzi ◽  
Michele Tizzoni ◽  
Matteo Chinazzi ◽  
Leo Ferres ◽  
Alessandro Vespignani ◽  
...  
Keyword(s):  
Social Inequalities ◽  
Mobile Phone Data ◽  
Government Interventions ◽  
Epidemic Dynamics ◽  
Santiago De Chile ◽  
Spatio Temporal ◽  
Systemic Health ◽  
The Impact ◽  
The City ◽  
Governmental Response

AbstractWe study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1st 2020, we estimate a detection rate of 102 cases per 1,000 infections (90% CI: [95 - 112 per 1,000]). We show that the introduction of a full lockdown on May 15th, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals’ mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The hetero-geneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic.

scholarly journals Analytical report of the 2016 dengue outbreak in Córdoba city, Argentina

2017 ◽  
Author(s):  
Camilo Rotela ◽  
Laura Lopez ◽  
María Frías Céspedes ◽  
Gabriela Barbas ◽  
Andrés Lighezzolo ◽  
...  
Keyword(s):  
Temporal Analysis ◽  
Control Measures ◽  
Remotely Sensed Data ◽  
Dengue Outbreak ◽  
High Incidence ◽  
Dengue Outbreaks ◽  
Spatio Temporal ◽  
The 1960S ◽  
Dengue Control ◽  
The City

After elimination of the Aedes aegypti vector in South America in the 1960s, dengue outbreaks started to reoccur during the 1990s; strongly in Argentina since 1998. In 2016, Córdoba City had the largest dengue outbreak in its history. In this article we report this outbreak including spatio-temporal analysis of cases and vectors in the city. A total of 653 dengue cases were recorded by the laboratory-based dengue surveillance system and georeferenced by their residential addresses. Case maps were generated from the epidemiological week 1 (beginning of January) to week 19 (mid-May). Dengue outbreak temporal evolution was analysed globally and three specific, high-incidence zones were detected using Knox analysis to characterising its spatio-temporal attributes. Field and remotely sensed data were collected and analysed in real time and a vector presence map based on the MaxEnt approach was generated to define hotspots, towards which the pesticide- based strategy was then targeted. The recorded pattern of cases evolution within the community suggests that dengue control measures should be improved.

scholarly journals Coworking in Lisbon. Experiences of collaboration and sharing on changing urban context

2020 ◽  
Author(s):  
Sofia Morgado ◽  
◽  
Elisabete Tomaz ◽  
Cristina Henriques ◽  
Patricia C. Melo ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Urban Context ◽  
Global Event ◽  
Recovery Capacity ◽  
Location Patterns ◽  
Spatio Temporal ◽  
Coworking Spaces ◽  
The Impact ◽  
The City ◽  
Different Parts

As innovative and creative ecosystems, Coworking Spaces (CWS) show high adaptability and recovery capacity in facing global event disruptions. This article is an exploratory study that aims to approach the development of coworking spaces in the city of Lisbon. It investigates the location patterns and adaptation of such ecosystems in Lisbon, by mapping the phenomenon and examining its spatio-temporal dynamics that was influenced by the urbanisation phases of the city, as well as the development and planning policies designed over the years. Starting from a broader view of CWS location in the city, this article moves on to present two particular cases, implemented in a different time and socio-economic circumstances in different parts of the city: LxFactory and The Creative Hub. This study is part of a broader contribution to COST Action (CA)18214 "The geography of the new workspaces and the impact on the periphery".

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