I hope this question is in the scope of CrossValidated (I think so because it is in the end about statistical analyses and machine learning, I am not looking for individual opinions but for reviews or surveys, for example based on pre-registered forecasts):
Modelling an epidemic like Covid-19 is difficult for well-known reasons such as behavioral changes or policy interventions (which lead to a time-dependent reproduction rate) or heterogeneity within the population (the reproduction rate differs between subpopulations).
Given these challenges and the relevance of providing accurate forecasts, numerous researchers have tried to provide well-working models to inform the public and policy-makers about the timing and the magnitude of future Covid-19 cases, hospitalizations and deaths.
After almost two years of the pandemic: is there yet a scientific consensus how well Covid-19 cases forecasts do work (e.g. based on pre-registered forecasts)? Any realizations if certain class of models work better than others?
Some further information:
There is a frequently cited article in the International Journal of Forecasting titled Forecasting for COVID-19 has failed
Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, consideration of only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects, and selective reporting are some of the causes of these failures.
However, at least one of the authors seems to be seen controversial in the community. I therefore do not know whether this article reflects the mainstream opinion in the field. Especially since there seem to be other articles available which are more positive (at least about short-term forecasts):
Conclusions Ensembles of multi-model forecasts can inform the policy response to the Covid-19 pandemic by assessing future resource needs and expected population impact of morbidity and mortality.