Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA.
Departamento de Ecología, Genética y Evolución, and Instituto IEGEBA (CONICET-UBA), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, C1428EHA, Buenos Aires, Argentina.
Nat Commun. 2022 Feb 22;13(1):996. doi: 10.1038/s41467-022-28231-w.
The spread of dengue and other arboviruses constitutes an expanding global health threat. The extensive heterogeneity in population distribution and potential complexity of movement in megacities of low and middle-income countries challenges predictive modeling, even as its importance to disease spread is clearer than ever. Using surveillance data at fine resolution from Rio de Janeiro, we document a scale-invariant pattern in the size of successive epidemics following DENV4 emergence. Using surveillance data at fine resolution following the emergence of the DENV4 dengue serotype in Rio de Janeiro, we document a pattern in the size of successive epidemics that is invariant to the scale of spatial aggregation. This pattern emerges from the combined effect of herd immunity and seasonal transmission, and is strongly driven by variation in population density at sub-kilometer scales. It is apparent only when the landscape is stratified by population density and not by spatial proximity as has been common practice. Models that exploit this emergent simplicity should afford improved predictions of the local size of successive epidemic waves.
登革热和其他虫媒病毒的传播构成了日益严重的全球健康威胁。在中低收入国家的特大城市中,人口分布的广泛异质性和潜在的复杂流动性给预测模型带来了挑战,尽管它对疾病传播的重要性比以往任何时候都更加明显。利用来自里约热内卢的精细分辨率监测数据,我们记录了继 DENV4 出现后连续发生的流行病规模的标度不变模式。利用 DENV4 登革热血清型在里约热内卢出现后的精细分辨率监测数据,我们记录了连续流行病规模的模式,该模式与空间聚集的规模无关。这种模式是由群体免疫和季节性传播的综合作用产生的,并且主要受亚公里尺度上人口密度变化的驱动。只有当景观按人口密度分层,而不是像通常那样按空间接近度分层时,这种模式才会显现出来。利用这种新出现的简单性的模型应该可以更好地预测连续流行波在当地的规模。