The epidemiology of Mayaro virus in the Americas: A systematic review and key parameter estimates for outbreak modelling

Mayaro virus (MAYV) is an arbovirus that is endemic to tropical forests in Central and South America, particularly within the Amazon basin. In recent years, concern has increased regarding MAYV’s ability to invade urban areas and cause epidemics across the region. We conducted a systematic literature review to characterise the evolutionary history of MAYV, its transmission potential, and exposure patterns to the virus. We analysed data from the literature on MAYV infection to produce estimates of key epidemiological parameters, including the generation time and the basic reproduction number, R0. We also estimated the force-of-infection (FOI) in epidemic and endemic settings. Seventy-six publications met our inclusion criteria. Evidence of MAYV infection in humans, animals, or vectors was reported in 14 Latin American countries. Nine countries reported evidence of acute infection in humans confirmed by viral isolation or reverse transcription-PCR (RT-PCR). We identified at least five MAYV outbreaks. Seroprevalence from population based cross-sectional studies ranged from 21% to 72%. The estimated mean generation time of MAYV was 15.2 days (95% CrI: 11.7–19.8) with a standard deviation of 6.3 days (95% CrI: 4.2–9.5). The per-capita risk of MAYV infection (FOI) ranged between 0.01 and 0.05 per year. The mean R0 estimates ranged between 2.1 and 2.9 in the Amazon basin areas and between 1.1 and 1.3 in the regions outside of the Amazon basin. Although MAYV has been identified in urban vectors, there is not yet evidence of sustained urban transmission. MAYV’s enzootic cycle could become established in forested areas within cities similar to yellow fever virus.

2/ The meaning of the statement that "Each study was allocated to two reviewers who independently screened abstracts and titles" (lines 107-8), most specifically the meaning of "study" in this context. I'd infer that this means the areas covered in the paper ("(i) the time of exposure to MAYV; (ii) the time of symptom onset....") but the statement could be misconstrued as written.
We have changed the word "study" to "article," as we included both peer-reviewed publications as well as grey literature. Indeed, one article can include more than one "study" or experiment. We apologize for any confusion (lines 108-9). Figure 3 needs slight editing as some of the data points (coloured circles) have been clipped at the bottom or top.

1/
We thank Review #1 for pointing this out, and we have edited the figure accordingly. Table 3 includes all (or perhaps nearly all) the results from the  table. These results should be in the table or text, but not both (at least not in full).

2/ The text above
We fully agree with this comment and have shortened the text above the table (lines 269-271).
3/ What are "Ileus antibodies" (Table 1). I'm not sure if I should know, but I don't. If this is not widely known an explanation should be given (or if it's a typo it should be corrected).
We thank Reviewer #1 for astutely catching this typo. We meant to write "Ilheus virus antibodies" and have corrected it. Ilheus virus is a flavivirus. It also appears in some of the tables in the supplement (line 462).

Summary and General Comments
I wonder if you could update the literature search beyond 11 January 2019?
We thank Reviewer #1 for this comment, but unfortunately do not think it would be feasible as nearly all of the authors are currently working on the COVID-19 pandemic. Since January 2019, Google Scholar alone is showing 1,210 results for Mayaro Virus or MAYV.
Introduction Lines 62-64. The sentence on serological cross-reactivity seems out of place here. I would recommend addressing this point around line 85 where the authors discuss the difficulty of diagnosis.
We thank Reviewer #2 for this recommendation and have moved the sentence (lines 82-85).

2.
Page 16 Table 3. The parameters are assumed to be the same for all studies. I was wondering if we could expect variations of the epidemiological parameters, for instance with the climatic conditions. (Mordecai Plos NTD 2013) discuss the impact of temperature on arbovirus transmission. Can we expect the mosquito lifetime to depend on the temperature and the season? If so, how would it change the different parameters?
Yes, we expect the parameters that depend on the vector, such as the extrinsic incubation period and mosquito lifetime, to change with temperature. We have added a section to the discussion to address this comment more fully (lines 386-394). [70]. Additionally, mosquito longevity depends on temperature and species [71]. It is unclear which species of mosquito is most important in MAYV transmission. Moreover, due to limited data, we had to pool information on six different species of mosquitoes to estimate the EIP. Thus, in order to estimate a temperature-dependent generation time distribution for MAYV, more vector data is needed.

Although the generation time distribution here was estimated independently of temperature, the component parameters that depend on the vector can vary based on temperature. For example, as temperature increases within a range acceptable to the vector, dengue virus replicates faster, decreasing the extrinsic incubation period (EIP) of Ae. aegypti
3. Figure 5 Page 17. The authors applied serocatalytic models to assess the force of infection (FOI) from age stratified seroprevalence studies. They tested models of timeindependent (endemicity) and time-dependent FOI. However, other models could explain the data, and from cross-sectional seroprevalence studies it is not possible to distinguish variations in time of the FOI or different exposures with age. For instance, the curve from Ecuador 1997 could also be due to an outbreak that happened in the 1990s and where adults were more exposed than children. It would be good to discuss this possibility, even more so that MAYV is often considered, as yellow fever, to be more likely to infect adults that are active near forested areas.
We agree with this comment and thank the Reviewer for mentioning it. We address the limitation of the cross-sectional data in lines 467-473.
Another limitation is that the use of cross-sectional seroprevalence studies did not allow us to investigate changes in the FOI over time or different exposures by age. One assumption of the catalytic models applied in our analysis is that there are no differences in exposure by age, which seems plausible when assuming no previous exposure such as in Brazil 2017 ( Fig 5B). However, we acknowledge that in some situations, adults may have had higher exposure to MAYV than children due to working exposure such as in forested areas. Unfortunately, we do not have access to such data.

Reviewer #3:
Minor suggestions: Details of the protocols are provided in the main text. Even though the used model is widely used by the community, details about the applied equations and model per se could be included in the Support Information.
We appreciate this comment. Serocatalytic models have been explained in the main text (lines 124-131) and, we have added a reference to line 125. Also, equations for generation time and R calculations have been included in main text (lines 139-152) and expanded in Supplementary Information. Methods for Phylogenetic analysis have been included in lines 154-164.