Unraveling the spatiotemporal spread of COVID-19 in Brazil through spatial network connectivity

Background: Describing and understanding the process of diffusion can allow local managers better plan emergence scenarios. Thus, the main aim of this study was to describe and unveil the spatiotemporal patterns of diffusion of the COVID-19 in Brazil from February 2020 until April 2021. Methods: This is a retrospective purely observational ecologic study including all notified cases and deaths. We used satellite-derived night light imagery and spatiotemporal Empirical Orthogonal Function analysis to quantify the spatial network structure of lighted development and the spatiotemporal transmission of the pathogen through the network. Results: The more populous state capitals within the largest network components presented higher frequency of deaths and earlier onset compared to the increasing numbers of smaller, less populous municipalities trending toward lower frequency of deaths and later onset. By week 48 2020, the full network was almost completely affected. Cases and deaths showed a distinct second wave of wider geographic expansion beginning in early November 2020. Conclusions: The spatiotemporal diffusion in Brazil was characterized by an intertwined process of overseas relocation, hierarchical network transmission and contagious effects. A rapid response as the immediate control of all ports, airports and borders combined with mandatory quarantine are critical to retard disease diffusion.

Global Spatiotemporal Transmission Patterns of Human Enterovirus 71 from 1963 to 2019

ABSTRACT EV71 can cause large outbreaks of HFMD and severe neurological diseases, which is regarded as a major threat to public health especially in Asia-Pacific regions. However, the global spatiotemporal spread of this virus has not been identified. In this study, we used large sequence datasets and a Bayesian phylogenetic approach to compare the molecular epidemiology and geographical spread patterns of different EV71 subgroups globally. The study found that subgroups of HFMD presented global spatiotemporal variation, subgroups B0, B1 and B2, have caused early infections in Europe and America, and then subgroups C1, C2, C3, C4 replaced B0-B2 as the predominant genotypes especially in Asia-Pacific countries. The dispersal patterns of genotype B and subgroup C4 showed the complicated routes in Asia and the source might in some Asia countries, while subgroups C1 and C2 displayed more strongly support pathways globally especially in Europe. This study found the predominant subgroup of EV71 and its global spatiotemporal transmission patterns, which may be beneficial to reveal the long term global spatiotemporal transmission patterns of human enterovirus 71 and carry out the HFMD vaccine development.

The Spatiotemporal Transmission Dynamics of COVID-19 among Multiple Regions: A Modeling Study in China

Current explosive outbreak of COVID-19 around the world is a complex spatiotemporal process with hidden interactions between viruses and humans. This study aims at clarifying the transmission patterns and the driving mechanism that contributed to the COVID-19 epidemics across the provinces of China. Thus a new dynamical transmission model is established by ordinary differential system. The model takes into account the hidden circulation of COVID-19 virus among/within humans, which incorporates the spatial diffusion of infection by parameterizing human mobility. Theoretical analysis indicates that the basic reproduction number is a unique epidemic threshold, which can unite infectivity in each region by human mobility, and can totally determine whether COVID-19 proceeds among multiple regions. By validating the model with real epidemic data in China, it is found that (1) if without any intervention, COVID-19 would overrun China within three months, resulting in more than 1.1 billion infections; (2) high frequency of human mobility can trigger COVID-19 diffusion across each province in China, no matter where the initial infection locates; (3) travel restrictions and other non-pharmaceutical interventions must be implemented simultaneously for disease control; and (4) infection sites in central and east (rather than west and northeast) of China would easily stimulate quick diffusion of COVID-19 in the whole country.

Revealing spatiotemporal transmission patterns and stages of COVID-19 in China using individual patients’ trajectory data

Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread.To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients’ trajectory data. By using innovative space–time analytics, this framework reveals the spatiotemporal patterns of patients’ mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.

Spatiotemporal transmission dynamics of co-circulating dengue, Zika, and chikungunya viruses in Fortaleza, Brazil: 2011–2017

The mosquito-borne viruses dengue (DENV), Zika (ZIKV), and chikungunya (CHIKV), now co-endemic in the Americas, pose growing threats to health worldwide. However, it remains unclear whether there exist interactions between these viruses that could shape their epidemiology. This study advances knowledge by assessing the transmission dynamics of co-circulating DENV, ZIKV, and CHIKV in the city of Fortaleza, Brazil. Spatiotemporal transmission dynamics of DENV, ZIKV, and CHIKV were analyzed using georeferenced data on over 210,000 reported cases from 2011 to 2017 in Fortaleza, Brazil. Local spatial clustering tests and space-time scan statistics were used to compare transmission dynamics across all years. The transmission of co-circulating viruses in 2016 and 2017 was evaluated at fine spatial and temporal scales using a measure of spatiotemporal dependence, the τ-statistic. Results revealed differences in the diffusion of CHIKV compared to previous DENV epidemics and spatially distinct transmission of DENV/ZIKV and CHIKV during the period of their co-circulation. Significant spatial clustering of viruses of the same type was observed within 14-day time intervals at distances of up to 6.8 km (p<0.05). These results suggest that arbovirus risk is not uniformly distributed within cities during co-circulation. Findings may guide outbreak preparedness and response efforts by highlighting the clustered nature of transmission of co-circulating arboviruses at the neighborhood level. The potential for competitive interactions between the arboviruses should be further investigated.

Inferring transmission trees to guide targeting of interventions against visceral leishmaniasis and post-kala-azar dermal leishmaniasis

Understanding of spatiotemporal transmission of infectious diseases has improved significantly in recent years. Advances in Bayesian inference methods for individual-level geo-located epidemiological data have enabled reconstruction of transmission trees and quantification of disease spread in space and time, while accounting for uncertainty in missing data. However, these methods have rarely been applied to endemic diseases or ones in which asymptomatic infection plays a role, for which novel estimation methods are required. Here, we develop such methods to analyse longitudinal incidence data on visceral leishmaniasis (VL), and its sequela, post-kala-azar dermal leishmaniasis (PKDL), in a highly endemic community in Bangladesh. Incorporating recent data on infectiousness of VL and PKDL, we show that while VL cases drive transmission when incidence is high, the contribution of PKDL increases significantly as VL incidence declines (reaching 55% in this setting). Transmission is highly focal: >85% of mean distances from inferred infectors to their secondary VL cases were <300m, and estimated average times from infector onset to secondary case infection were <4 months for 90% of VL infectors, but up to 2.75yrs for PKDL infectors. Estimated numbers of secondary VL cases per VL and PKDL case varied from 0-6 and were strongly correlated with the infector’s duration of symptoms. Counterfactual simulations suggest that prevention of PKDL could have reduced VL incidence by up to a quarter. These results highlight the need for prompt detection and treatment of PKDL to achieve VL elimination in the Indian subcontinent and provide quantitative estimates to guide spatiotemporally-targeted interventions against VL.Significance StatementAlthough methods for analysing individual-level geo-located disease data have existed for some time, they have rarely been used to analyse endemic human diseases. Here we apply such methods to nearly a decade’s worth of uniquely detailed epidemiological data on incidence of the deadly vector-borne disease visceral leishmaniasis (VL) and its secondary condition, post-kala-azar dermal leishmaniasis (PKDL), to quantify the spread of infection around cases in space and time by inferring who infected whom, and estimate the relative contribution of different infection states to transmission. Our findings highlight the key role long diagnosis delays and PKDL play in maintaining VL transmission. This detailed characterisation of the spatiotemporal transmission of VL will help inform targeting of interventions around VL and PKDL cases.