Program9.00 AM Introduction: origin and motivation of the session. Gabriel Fabricius (INIFTA-CONICET-UNLP, La Plata, Argentina)
9.10 AM Modelling to inform strategy and tactics in Australia’s COVID-19 response. Jodie Mc Vernon (Doherty Institute, Melbourne, Australia)
9.30 AM The experience with epidemiological indicators to advise decision making during the COVID-19 pandemic in Brazil. Daniel Villela (Fiocruz, Rio de Janeiro, Brazil)
9.50 AM The turning point and end of an expanding epidemic cannot be precisely forecast. Saul Ares (CSIC, Madrid, Spain)
10.10 AM Modeling Covid-19 in Costa Rica: A Multilayer Network Approach. Fabio Sanchez (School of Mathematics-CIMPA, University of Costa Rica, Costa Rica)
10.30 Discussion
Multidisciplinary science and scientific advising for public decision making. Lessons from the COVID-19 pandemic.During the current COVID-19 pandemic, and especially at the early times facing the emergency, scientists were called to duty to confront the pandemic on two fronts: the technical and the decision-making fronts. Science was well prepared and organized for the first task: COVID-19 diagnosis tests were developed very fast, together with new and enhanced antiviruses protective equipment, starting the development of several vaccines with different technologies in record time. In public policy advising matters, the science contribution is not so clear and it is difficult to measure. A probable factor contributing to this problem is that emerging natural problems are not divided into disciplines and subdisciplines, as science is. Epidemics are phenomena that exceed the union of disciplines since many of their difficulties stem from intricacies in the region of knowledge where the disciplines fade out, a sort of nobody’s land. In addition to the need for an interdisciplinary approach, fast communication between scientists, the public health system and the government is required.
The meeting proposes to examine different experiences that occurred during the COVID-19 pandemic and the possibilities and difficulties to integrate into decision processes seeking to discover the forms in which interdisciplinary efforts can be articulated and promoted worldwide. Resúmenes- Abstracts: Introduction: origin and motivation of the session. G.FabriciusI will make a brief introduction commenting on some activities carried out during the COVID19 pandemic. In particular, the monitoring of the epidemic in Argentina performed by the Infectious Diseases Modelling Network during 2020, and the series of "Interdisciplinary Dialogues around the COVID-19 pandemic", conducted within the framework of the Network for Translational Research in Health (
https://rits.conicet.gov.ar/) during 2021. Some of the questions and concerns that emerged from those activities motivated the organization of the present session.
Modelling to inform strategy and tactics in Australia’s COVID-19 response. J. Mc VernonThe Western Pacific Region was unique globally in its experience of the COVID-19 pandemic, with many island nations including Australia using strict border controls to delay entry of the virus over an extended period. This initial phase of ‘anticipation’ was followed by a strong suppression strategy achieved through active case and contact management, supported by stringent social restrictions as required. Effective ‘elimination’ was sustained through most subnational jurisdictions through to mid 2021, at which point ‘vaccination’ provided a pathway to reconnecting within and beyond national borders. Importation of the highly transmissible Omicron variant rapidly transitioned Australia’s COVID-19 epidemic from low to high prevalence, although burden was substantively mitigated by high prior vaccine coverage. By late 2022, high ‘saturation’ of infection and vaccination coverage had been achieved, making the population relatively resilient to the emergence of BA4/5. This presentation will review the role of modelling in guiding strategic policy decision making within and between these four stages of Australia’s pandemic response.
The experience with epidemiological indicators to advise decision making during the COVID-19 pandemic in Brazil. D. VillelaBrazil collected records of COVID-19 cases into national surveillance databases during the pandemic. From the start, there was a need to establish good epidemiological indicators to advise decision-making oriented to mitigate the pandemic's impact. This talk will discuss lessons learned during the process. Attention will be given to highlighting indicators that convey the incidence of severe cases, the impact of policies, including vaccination, challenges involving the volume of data, and periods with missing notification data.
The turning point and end of an expanding epidemic cannot be precisely forecast. S. AresCertain generic features of epidemics are well-illustrated by mathematical models, which can be remarkably good at reproducing empirical data through suitably chosen parameters. However, this does not assure a good job anticipating the forthcoming stages of the process. The future of ongoing epidemics is so sensitive to parameter values that predictions are only meaningful within a narrow time window and in probabilistic terms, much as what we are used to in weather forecasts. Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained; neither can the end of the epidemic after the turning point. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. However, these shortcomings do not render epidemiological models useless: short term predictions can be valuable and, moreover, models are an effective tool to compare scenarios and design action strategies.
Modeling Covid-19 in Costa Rica: A Multilayer Network Approach. F. SánchezDuring the 2020 Covid-19 pandemic in Costa Rica, different groups developed modelling techniques to understand the transmission dynamics of the disease. The modelling team, EpiMEC, developed deterministic models early in the pandemic to provide insight into the early disease dynamics. During the same period, the team developed a multilayer network model that allowed for more flexibility and included preventive measures and interventions from health authorities in the country. The model provided information for decision-makers in the country during the pandemic in Costa Rica.
Add to your schedule to access the video stream
