Spatio-temporal synchrony of influenza in cities across Israel: the "Israel is one city" hypothesis

Lockdown Contained the Spread of 2019 Novel Coronavirus Disease in Huangshi City, China: Early Epidemiological Findings

Background

To control the spread of 2019 novel coronavirus disease (COVID-19), China sealed Wuhan on Jan 23, 2020 and soon expanded lockdown to other twelve cities in Hubei province. We aimed to describe the epidemiological characteristics in one of the cities and highlight the effect of current implemented lockdown and nonpharmaceutical interventions.

Methods

We retrieved data of reported cases in Huangshi and Wuhan from publicly available disease databases. Local epidemiological data on suspected or confirmed cases in Huangshi were collected through field investigation. Epidemic curves were constructed with data on reported and observed cases.

Results

The accumulated confirmed COVID-19 cases and fatality in Huangshi were reported to be 1015 and 3.74% respectively, compared with 50006 and 5.08% in Wuhan till Mar 27, 2020. Right after Jan 24, the epidemic curve based on observed cases in Huangshi became flattened. Feb 1, 2020 was identified as the “turning point” as the epidemic in Huangshi faded soon afterwards. COVID-19 epidemic was characterized by mild cases in Huangshi, accounting for 82.66% of total cases. Moreover, 50 asymptomatic infections were identified in adults and children. Besides, we found confirmed cases in 19 familial clusters and 21 health care workers, supporting inter-human transmission.

Conclusions

Our study reported the temporal dynamics and characteristics of the COVID-19 epidemic in Huangshi city, China, across the unprecedented intervention. Such new epidemiological inference might provide further guidance on current lockdown measures in high-risk cities and, subsequently, help improve public health intervention strategies against the pandemic on the country and global levels.

Analysis of multi-level spatial data reveals strong synchrony in seasonal influenza epidemics across Norway, Sweden, and Denmark

Population structure, spatial diffusion, and climatic conditions mediate the spatiotemporal spread of seasonal influenza in temperate regions. However, much of our knowledge of these dynamics stems from a few well-studied countries, such as the United States (US), and the extent to which this applies in different demographic and climatic environments is not fully understood. Using novel data from Norway, Sweden, and Denmark, we applied wavelet analysis and non-parametric spatial statistics to explore the spatiotemporal dynamics of influenza transmission at regional and international scales. We found the timing and amplitude of epidemics were highly synchronized both within and between countries, despite the geographical isolation of many areas in our study. Within Norway, this synchrony was most strongly modulated by population size, confirming previous findings that hierarchical spread between larger populations underlies seasonal influenza dynamics at regional levels. However, we found no such association when comparing across countries, suggesting that other factors become important at the international scale. Finally, to frame our results within a wider global context, we compared our findings from Norway to those from the US. After correcting for differences in geographic scale, we unexpectedly found higher levels of synchrony in Norway, despite its smaller population size. We hypothesize that this greater synchrony may be driven by more favorable and spatially uniform climatic conditions, although there are other likely factors we were unable to consider (such as reduced variation in school term times and differences in population movements). Overall, our results highlight the importance of comparing influenza spread at different spatial scales and across diverse geographic regions in order to better understand the complex mechanisms underlying disease dynamics.

Linking influenza epidemic onsets to covariates at different scales using a dynamical model

Background

Evaluating the factors favoring the onset of influenza epidemics is a critical public health issue for surveillance, prevention and control. While past outbreaks provide important insights for understanding epidemic onsets, their statistical analysis is challenging since the impact of a factor can be viewed at different scales. Indeed, the same factor can explain why epidemics are more likely to begin (i) during particular weeks of the year (global scale); (ii) earlier in particular regions (spatial scale) or years (annual scale) than others and (iii) earlier in some years than others within a region (spatiotemporal scale).

Methods

Here, we present a statistical approach based on dynamical modeling of infectious diseases to study epidemic onsets. We propose a method to disentangle the role of covariates at different scales and use a permutation procedure to assess their significance. Epidemic data gathered from 18 French regions over six epidemic years were provided by the Regional Influenza Surveillance Group (GROG) sentinel network.

Results

Our results failed to highlight a significant impact of mobility flows on epidemic onset dates. Absolute humidity had a significant impact, but only at the spatial scale. No link between demographic covariates and influenza epidemic onset dates could be established.

Discussion

Dynamical modeling presents an interesting basis to analyze spatiotemporal variations in the outcome of epidemic onsets and how they are related to various types of covariates. The use of these models is quite complex however, due to their mathematical complexity. Furthermore, because they attempt to integrate migration processes of the virus, such models have to be much more explicit than pure statistical approaches. We discuss the relation of this approach to survival analysis, which present significant differences but may constitute an interesting alternative for non-methodologists.