This introduction to the special section of South Asia: Journal of South Asian Studies, titled 'Religious Minorities in Pakistan', reviews the existing scholarship on this topic, points out gaps in the research, and discusses problematic notions and assumptions in both popular and academic discourses on minorities. Furthermore, it attempts a definition of the term 'religious minority', demonstrates its extensive entanglement with the question of caste-a characteristic specific to the South Asian case-and situates this discourse within broader debates about post-colonial state-building, the history of sectarianism in the region, contestations over religious authority, and the striving for a coherent political and cultural identity in Pakistan, the second-largest Muslim nation in the world.

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This paper seeks to illuminate the intellectual impact of the Iranian Revolution of 1979 among Pakistani Shiʻas by focusing on Sayyid ʻArif Husain al-Husaini, the dominating Shiʻi leader of the 1980s. In particular, I am interested in exploring how al-Husaini adapted hallmark themes of the Iranian revolutionary message, such as Muslim unity or political leadership of the religious scholars (ʻulama), to the specific circumstances of Pakistan. Crucial for such processes of translation was not only pressure from the Pakistani state but rather internal challenges and divisions among the Shiʻi community. While al-Husaini could draw on a strong, indigenous tradition of political mobilisation, his revolutionary ʻthird waveʼ of Shiʻi thought sat uncomfortably between Lucknow-educated traditionalists and Najaf-trained reformers who shied away from getting entangled in these novel forms of politics. By drawing on biographical accounts and al-Husaini's speeches in Urdu, I trace how his revolutionary rhetoric had to accommodate thorny local issues such as sectarianism, South Asian mourning traditions or the lack of an established Shiʻi clerical hierarchy in Pakistan.

Influenza infections are challenging to monitor at the population level due to a high proportion of mild and asymptomatic cases and confounding of symptoms with other common circulating respiratory diseases, including COVID-19. Alternate methods capable of tracking cases outside of clinical reporting infrastructure could improve monitoring of influenza transmission dynamics. Influenza shedding into wastewater represents a promising source of information where quantification is unbiased by testing or treatment-seeking behaviors. We quantified influenza A and B virus loads from influent at Switzerland's three largest wastewater treatment plants, serving about 12% of the Swiss population. We estimated trends in infection incidence and the effective reproductive number Re in these catchments during a 2021/22 epidemic and compared our estimates to clinical influenza surveillance data. We showed that wastewater-based incidence is better aligned with catchment-level confirmed cases than national ILI, and that only the wastewater data capture a peak in incidence in December 2021. We further estimated Re to have been below 1.05 after introduction of work from home measures in December 2021 and above 0.97 after these measures were relaxed in two out of three catchments based on wastewater data. The third catchment yielded qualitatively the same results, although with wider confidence intervals. The confirmed-case data yielded comparatively less precise estimates that include 1 before and during the period of measures. On the basis of this research we developed an online dashboard for wastewater-based influenza surveillance in Switzerland where we will continue to monitor the onset and dynamics of the 2022/23 flu season.

AIMS OF THE STUDY: Wastewater-based epidemiology has contributed significantly to the comprehension of the dynamics of the current COVID-19 pandemic. Its additional value in monitoring SARS-CoV-2 circulation in the population and identifying newly arising variants independently of diagnostic testing is now undisputed. As a proof of concept, we report here correlations between SARS-CoV-2 detection in wastewater and the officially recorded COVID-19 case numbers, as well as the validity of such surveillance to detect emerging variants, exemplified by the detection of the B.1.1.529 variant Omicron in Basel, Switzerland. METHODS: From July 1 to December 31, 2021, wastewater samples were collected six times a week from the inflow of the local wastewater treatment plant that receives wastewater from the catchment area of the city of Basel, Switzerland, comprising 273,075 inhabitants. The number of SARS-CoV-2 RNA copies was determined by reverse transcriptase-quantitative PCR. Spearman’s rank correlation coefficients were calculated to determine correlations with the median seven-day incidence of genome copies per litre of wastewater and official case data. To explore delayed correlation effects between the seven-day median number of genome copies/litre wastewater and the median seven-day incidence of SARS-CoV-2 cases, time-lagged Spearman’s rank correlation coefficients were calculated for up to 14 days. RNA extracts from daily wastewater samples were used to genotype circulating SARS-CoV-2 variants by next-generation sequencing. RESULTS: The number of daily cases and the median seven-day incidence of SARS-CoV-2 infections in the catchment area showed a high correlation with SARS-CoV-2 measurements in wastewater samples. All correlations between the seven-day median number of genome copies/litre wastewater and the time-lagged median seven-day incidence of SARS-CoV-2 cases were significant (p<0.001) for the investigated lag of up to 14 days. Correlation coefficients declined constantly from the maximum of 0.9395 on day 1 to the minimum of 0.8016 on day 14. The B.1.1.529 variant Omicron was detected in wastewater samples collected on November 21, 2021, before its official acknowledgement in a clinical sample by health authorities. CONCLUSIONS: In this proof-of-concept study, wastewater-based epidemiology proved a reliable and sensitive surveillance approach, complementing routine clinical testing for mapping COVID-19 pandemic dynamics and observing newly circulating SARS-CoV-2 variants.