Quantifying arbovirus disease and transmission risk at the municipality level in the Dominican Republic: the inception of Rm
Authors: Kingston Rhys , Routledge Isobel , Bhatt Samir , Bowman Leigh R 
Published: July 2020

Arboviruses remain a significant cause of morbidity, mortality and economic cost across the global human population. Epidemics of arboviral disease, such as Zika and dengue, also cause significant disruption to health services at local and national levels. This study examined 2014-16 Zika and dengue epidemic data at the sub-national level to characterise transmission across the Dominican Republic.

For each municipality, spatio-temporal mapping was used to characterise disease burden, while data were age and sex standardised to quantify burden distributions among the population. In separate analyses, time-ordered data were combined with the underlying disease migration interval distribution to produce a network of likely transmission chain events, displayed using transmission chain likelihood matrices. Finally, municipal-specific reproduction numbers (Rm) were established using a Wallinga-Teunis matrix.

Dengue and Zika epidemics peaked during weeks 39-52 of 2015 and weeks 14-27 of 2016 respectively. At the provincial level, dengue attack rates were high in Hermanas Mirabal and San José de Ocoa (58.1 and 49.2 cases per 10,000 population respectively), compared with the Zika burden, which was highest in Independencia and San José de Ocoa (21.2 and 13.4 cases per 10,000 population respectively). Across municipalities, high disease burden was observed in Cotui (622 dengue cases per 10,000 population) and Jimani (32 Zika cases per 10,000 population). Municipal infector-infectee transmission likelihood matrices identified six 0% likelihood transmission events throughout the dengue epidemic and one 0% likelihood transmission event during the Zika epidemic. Municipality reproduction numbers (Rm) were consistently higher, and persisted for a greater duration during the Zika epidemic (Rm = 1.0), than during the dengue epidemic (Rm = <1.0).

This research highlights the importance of disease surveillance in land-border municipalities as an early warning for infectious disease transmission. It also demonstrates that a high number of importation events are required to sustain transmission in endemic settings, and vice versa for newly emerged diseases. The inception of a novel epidemiological metric, Rm, reports transmission risk using standardised spatial units, and can be used to identify high transmission risk municipalities to better focus public health interventions for dengue, Zika, and other infectious diseases.

Author Summary

Arboviruses remain a significant cause of morbidity, mortality and economic cost. Between the years 2014-16, two large arbovirus outbreaks occurred in the Dominican Republic. The first was a wave of dengue cases, followed by a large Zika epidemic. Using various mathematical modelling and geospatial approaches, a number of analyses were undertaken to both characterise the pattern of disease transmission and identify high-burden municipalities. Throughout the process, a novel metric was developed: the Rm. This parameter was used to identify the transmission potential of any given municipality to surrounding municipalities, where >1.0 is high transmission risk, and <1.0 is low transmission risk. This is useful as it provides a standardised approach to determine where public health resources might be focussed to better impact ongoing disease transmission. Additionally, analyses demonstrated the importance of increased disease surveillance in municipalities that share land borders with neighbouring countries, and how relatively few disease importation events can spark and sustain an epidemic.