Are COVID-19 models blind to the social determinants of health? A systematic review protocol

IntroductionInfectious disease models are important tools to inform public health policy decisions. These models are primarily based on an average population approach and often ignore the role of social determinants in predicting the course of a pandemic and the impact of policy interventions. Ignoring social determinants in models may cause or exacerbate inequalities. This limitation has not been previously explored in the context of the current pandemic, where COVID-19 has been found to disproportionately affect marginalised racial, ethnic and socioeconomic groups. Therefore, our primary goal is to identify the extent to which COVID-19 models incorporate the social determinants of health in predicting outcomes of the pandemic.Methods and analysisWe will search MEDLINE, EMBASE, Cochrane Library and Web of Science databases from December 2019 to August 2020. We will assess all infectious disease modelling studies for inclusion of social factors that meet the following criteria: (a) focused on human spread of SARS-CoV-2; (b) modelling studies; (c) interventional or non-interventional studies; and (d) focused on one of the following outcomes: COVID-19-related outcomes (eg, cases, deaths), non-COVID-19-related outcomes (ie, impacts of the pandemic or control policies on other health conditions or health services), or impact of the pandemic or control policies on economic outcomes. Data will only be extracted from models incorporating social factors. We will report the percentage of models that considered social factors, indicate which social factors were considered, and describe how social factors were incorporated into the conceptualisation and implementation of the infectious disease models. The extracted data will also be used to create a narrative synthesis of the results.Ethics and disseminationEthics approval is not required as only secondary data will be collected. The results of this systematic review will be disseminated through peer-reviewed publication and conference proceedings.PROSPERO registration numberCRD42020207706.

CXCL9-expressing tumor-associated macrophages: new players in the fight against cancer

Tumor-associated macrophages (TAMs) are among the main contributors to immune suppression in the tumor microenvironment, however, TAM depletion strategies have yielded little clinical benefit. Here, we discuss the concept that TAMs are also key regulators of anti-PD(L)-1-mediated CD8 T cell-dependent immunity. Emerging data suggest that expression of the chemokine CXCL9 by TAMs regulates the recruitment and positioning of CXCR3-expressing stem-like CD8 T (Tstem) cells that underlie clinical responses to anti-PD(L)-1 treatment. We evaluate clinical and mechanistic studies that establish relationships between CXCL9-expressing TAMs, Tstem and antitumor immunity. Therapies that enhance anti-PD(L)-1 response rates must consider TAM CXCL9 expression. In this perspective, we discuss opportunities to enhance the frequency and function of CXCL9 expressing TAMs and draw on comparative analyzes from infectious disease models to highlight potential functions of these cells beyond Tstem recruitment.

Responsible modelling: Unit testing for infectious disease epidemiology

Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID- 19 and Ebola and endemic diseases such as malaria and tuberculosis. Yet a single coding bug may bias results, leading to incorrect conclusions and wrong actions that could cause avoidable harm. We are ethically obliged to ensure our code is as free of error as possible. Unit testing is a coding method to avoid such bugs, but unit testing is rarely used in epidemiology. We demonstrate through simple examples how unit testing can handle the particular quirks of infectious disease models.

Geographically dependent individual-level models for infectious diseases transmission

Summary Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods.