Wastewater genomic surveillance to track infectious disease-causing pathogens in low-income countries: Advantages, limitations, and perspectives.

The emergence of infectious diseases, particularly those caused by respiratory pathogens like COVID-19 and influenza viruses, poses a significant threat to public health, especially in the context of climate change. Vulnerable variants and major pathogenicities are appearing, leading to a wide range of illnesses and increased morbidity. Wastewater genomic surveillance represents a cost-effective and a crucial tool for tracking infectious diseases, particularly in regions where clinical testing resources might be limited or inadequate. However, there are numerous limitations that need to be addressed in order to enhance its effectiveness for monitoring a wide range of pathogens. The current study uses this approach for the first time in Morocco to monitor the epidemiology of SARS-CoV-2 and Influenza A, B and RSV virus infections during the third wave of COVID-19 caused by the Omicron variant. The virome was concentrated from wastewater collected from two sewersheds of two cities, Agadir and Inezgane, using the the polyethylene glycol (PEG)/NaCl method. All 26 samples from both cities exhibited positive results for SARS-CoV-2, indicating varying viral loads. In the case of the Influenza A virus, four samples tested positive in Inezgane. However, no detection of Influenza B or RSV was observed in any of the samples. The estimated SARS-CoV-2 viral RNA copy numbers observed were then used to estimate the number of infected individuals using the SEIR model. The estimated number of cases correlates positively with the number of reported cases. Next Generation Sequencing showed that samples contain the following two variants: BA.1 and BA.2 that have been detected in clinical samples. In the case of Influenza A, clinical samples revealed a mild presence of the influenza virus subtype A(H3N2). This study demonstrates the effectiveness and feasibility of wastewater genomic surveillance in monitoring pathogens such as SARS-CoV-2 in Morocco. This approach can become an even more powerful tool for monitoring and predicting the spread of infectious diseases by addressing several key considerations. These include enhancing data collection methods, making environmental corrections for factors affecting RNA stability in wastewater, and refining mathematical models to improve their accuracy in predicting the number of infected cases. Incorporating statistical and machine learning models can further enhance the precision of these predictions.