Correlation of the basic reproduction number (R0) and eco-environmental variables in Colombian municipalities with chikungunya outbreaks during 2014-2016

Abstract

Chikungunya virus (CHIKV) emerged in Colombia in 2014 into a population presumed fully susceptible. This resulted in a quick and intense spread across Colombia, resulting in an epidemic that affected an estimated 450,000 people. The reported Colombian cases accounted for over 49% of all the cases reported to the PAHO. Eco-environmental factors are known to be associated with the spread of arboviruses such as CHIKV, and likely contribute to the differences in transmission profiles that were observed across several municipalities. To determine the association of eco-environmental factors and CHIKV, the basic reproduction number (R0) in 85 municipalities, which accounted for 65.6% of reported CHIKV cases in Colombia, was estimated. Estimates of R0 ranged from 1 to 9, where over 76% of municipalities had R0 values between 1 and 2. When we looked at the distribution of R0, the cumulative proportions were 20% with R0>2, 14% with R0>3, and 9% with R0>4. Next, we determined that there were different patterns of correlation between environmental and/or ecological variables and R0 when we considered different R0 lower-thresholds. Broadly, we found that temperature-related variables are significantly and positively correlated to R0 regardless of the lower threshold, while other variables like duration of outbreak and size of the urban area are inversely related to R0. Specifically, we conclude that high values of temperature-related variables where R0 > 1 will result in a fast growth of cases in a shorter time period (with faster cessation of outbreak transmission) but will result overall in a fewer total cases compared to outbreak areas (R0 > 1, but classified as lower). Thus, in the absence of vector control, a less explosive outbreak may be more advantageous for the virus in terms of transmission.

Estimating and explaining the spread of COVID-19 at the county level in the USA

“…Using R 0 estimates to design public health policies is predicated on the assumption that the R 0 values at the start of the epidemic reflect properties of the infective agent and population, and therefore predict the potential rate of spread of the disease. Estimates of R 0 , however, might not predict future risks if (i) they are measured after perceived risks have generated government actions or pre-emptive personal measures to reduce the spread rate 5 – 7 , (ii) they are driven by stochastic events, such as super-spreading 8 , 9 , or (iii) they are driven by social or environmental conditions that are likely to change between the time of initial epidemic and the future time for which public health interventions are designed 10 , 11 . To address these potential limitations for using R 0 to design public health policies and future risks of spread, we investigated possible underlying causes for variation in estimates of R 0 among counties: if the causes are unlikely to change in the future, then so too are values of R 0 unlikely to change.…”

Estimating and explaining the spread of COVID-19 at the county level in the USA

“…Using R 0 estimates to design public health policies is predicated on the assumption that the R 0 values at the start of the epidemic reflect properties of the infective agent and population, and therefore predict the potential rate of spread of the disease. Estimates of R 0 , however, might not predict future risks if (i) they are measured after public and private actions have been taken to reduce spread 5,6 , (ii) they are driven by stochastic events, such as super-spreading 7,8 , or (iii) they are driven by social or environmental conditions that are likely to change between the time of initial epidemic and the future time for which public health interventions are designed 9,10 . To address these potential limitations for using R 0 to design public health policies and future risks of spread, we investigated possible underlying causes for variation in estimates of R 0 among counties: if the causes are unlikely to change in the future, then so too are values of R 0 unlikely to change.…”

The tortoise strategy as an arbovirus fitness phenotype within the mosquito as revealed by a novel formulation of age-structured vectorial capacity

“…Overall, we demonstrate that (provided the biting rate is high enough) EIPmin results in a longer window of opportunity compared to EIPmax and is thus at least as, if not more capable of producing an outbreak than the EIPmax scenario, though the magnitude of the initial outbreak may be less. However, this same TotH model recently described macro-transmission dynamics in Colombia, where it was demonstrated that slow burn-in epidemics actually resulted in cumulatively more cases and higher R0 values than in initially explosive outbreaks [49]. Thus, the EIPmin scenario may lead, at a macro-level, to more prolonged transmission as it would results in a slower burn-in than the EIPmax scenario.…”