Short-term forecasts of infectious disease spread are a critical component in
risk evaluation and public health decision making. While different models for
short-term forecasting have been developed, open questions about their relative
performance remain. Here, we compare short-term probabilistic forecasts of
popular mechanistic models based on the renewal equation with forecasts of
statistical time series models. Our empirical comparison is based on data of
the daily incidence of COVID-19 across six large US states over the first
pandemic year. We find that, on average, probabilistic forecasts from
statistical time series models are overall at least as accurate as forecasts
from mechanistic models. Moreover, statistical time series models better
capture volatility. Our findings suggest that domain knowledge, which is
integrated into mechanistic models by making assumptions about disease
dynamics, does not improve short-term forecasts of disease incidence. We note,
however, that forecasting is often only one of many objectives and thus
mechanistic models remain important, for example, to model the impact of
vaccines or the emergence of new variants.

本研究比较了基于更新方程的机械模型和统计时间序列模型的短期概率预测，发现统计时间序列模型整体上至少与机械模型预测同样准确，并且更好地捕捉波动性，表明机械模型中融入的领域知识并不能提高疾病发病率的短期预测能力。

使用机械和统计时间序列模型进行 COVID-19 发病率的短期概率预测比较

A comparison of short-term probabilistic forecasts for the incidence of  COVID-19 using mechanistic and statistical time series models

Diffusion processes are governed by external triggers and internal dynamics
in complex systems. Timely and cost-effective control of infectious disease
spread critically relies on uncovering the underlying diffusion mechanisms,
which is challenging due to invisible causality between events and their
time-evolving intensity. We infer causal relationships between infections and
quantify the reflexivity of a meta-population, the level of feedback on event
occurrences by its internal dynamics (likelihood of a regional outbreak
triggered by previous cases). These are enabled by our new proposed model, the
Latent Influence Point Process (LIPP) which models disease spread by
incorporating macro-level internal dynamics of meta-populations based on human
mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
causal inference, outbreaks are more likely driven by statewide global
diffusion over time, leading to complex behavior of disease spread. In terms of
reflexivity, precursory growth and symmetric decline in populous regions is
attributed to slow but persistent feedback on preceding outbreaks via
inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
model reveals probabilistic causal relationships between discrete events based
on intra- and inter-group dynamics and also covers direct and indirect
diffusion processes (contact-based and vector-borne disease transmissions).

通过新提出的潜在影响点过程模型 (Latent Influence Point Process，LIPP) 来量化传染病在人口密集地区的内部动态对事件发生的反馈（触发区域性爆发疫情的前期病例存在的可能性），并研究了昆士兰州 15 年的登革热病例，发现爆发主要由州间全局扩散所驱动，而密集地区的疫情受到前期流行病的缓慢而持续的反馈，而边缘区域的疫情则受到快速但不稳定的反馈。该模型揭示了不同组之间的直接和间接扩散过程以及基于接触和向量传播疾病的概率因果关系。