Models and modelling practices for assessing the impact of outbreak response interventions to human vaccine-preventable diseases (1970-2019) - A systematic review

Background Mathematical modelling can aid outbreak response decision-making. However, this would require collaboration among model developers, decision-makers, and local experts to incorporate appropriate realism. We conducted a systematic review of modelling studies on human vaccine-preventable disease (VPD) outbreaks to identify patterns in modelling practices among collaborations. We complemented this with a mini review of eligible studies from the foot-and-mouth disease (FMD) literature. Methods Three databases were searched for studies published during 1970-2019 that applied models to assess the impact of an outbreak response. Per included study, we extracted data on author affiliation type (academic institution, governmental, and non-governmental organizations), whether at least one author was affiliated to the country studied, interventions, and model characteristics. Furthermore, the studies were grouped into two collaboration types: purely academic (papers with only academic affiliations), and mixed (all other combinations) to help investigate differences in modelling patterns between collaboration types in the human disease literature. Additionally, we compared modelling practices between the human VPD and FMD literature. Results Human VPDs formed 228 of 253 included studies. Purely academic collaborations dominated the human disease studies (56%). Notably, mixed collaborations increased in the last seven years (2013 - 2019). Most studies had an author in the country studied (75.2%) but this was more likely among the mixed collaborations. Contrasted to the human VPDs, mixed collaborations dominated the FMD literature (56%). Furthermore, FMD studies more often had an author affiliated to the country studied (92%) and used complex model design, including stochasticity, and model parametrization and validation. Conclusion The increase in mixed collaboration studies over the past seven years could suggest an increase in the uptake of modelling for outbreak response decision-making. We encourage more mixed collaborations between academic and non-academic institutions and the involvement of locally affiliated authors to help ensure that the studies suit local contexts. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Protocols ### Funding Statement JMA and JRCP are supported by Department of Science and Innovation, South Africa and National Research Foundation, South Africa. Any opinion, finding, and conclusion or recommendation expressed in this material is that of the authors, and the NRF does not accept any liability in this regard. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data extracted from the studies are available from at the following link https://osf.io/dmvst/?view_only=b50a8d3ec21b4a07b7977d0f56e79fc3