Qasim Bukhari

Bio: Qasim Bukhari is an academic researcher from McGovern Institute for Brain Research. The author has contributed to research in topics: Resting state fMRI & Default mode network. The author has an hindex of 7, co-authored 12 publications receiving 342 citations. Previous affiliations of Qasim Bukhari include University of Coimbra & Massachusetts Institute of Technology.

Will Coronavirus Pandemic Diminish by Summer

Abstract: The novel coronavirus (2019-nCoV) has spread rapidly to multiple countries and has been declared a pandemic by the World Health Organization. While influenza virus has been shown to be affected by weather, it is unknown if COVID19 is similarly affected. In this work, we analyze the patterns in local weather of the regions affected by 2019-nCoV virus until March 22, 2020. So far, 83% of testing have been conducted in non-tropical countries (30N and above) and 90% of the 2019-nCoV cases have been recorded in the same countries within a temperature range of 3 to 17C. Similarly, ~72% of the measurements were done in countries with humidity between 3 and 9g/m3 and 90% of the cases were observed within the same range of absolute humidity (AH). The higher number of tests and global connectivity of the northern-cooler countries may explain the difference in number of confirmed 2019-nCoV cases between cooler and warmer-humid regions. Nonetheless, several countries between 30N and 30S such as Australia, UAE, Qatar, Singapore, Bahrain, Qatar and Taiwan have performed extensive testing per capita and the number of positive 2019-nCoV cases per capita are lower in these countries compared to several European countries and the US. Therefore, even though currently available data is skewed by minimal testing per capita in many tropical countries, it is possible that weather plays a role in the spread of 2019-nCoV which warrants an investigation. In the last 10 days, thousands of new cases have been documented in regions with T >18C suggesting that the role of warmer temperature in slowing the spread of the 2019-nCoV, as suggested earlier might only be observed, if at all, at much higher temperatures. Unlike temperature, however, the range of AH across which most of the cases have been documented has consistently been between 3 and 9g/m3​. Current data, although limited, suggests that it is extremely unlikely that the spread of 2019-nCoV would slow down in the USA or Europe, due to environmental factors, because a large number of cases have already been reported in the range of AH and T experienced by these regions for most part of the year. Given previous associations between viral transmission and humidity and the range of AH across which the majority of the 2019-nCoV cases have been observed till date, the role of absolute humidity merits further investigation with laboratory experiments studying the sensitivity of 2019-nCoV across a range of temperature and humidity conditions. On the other hand, if, new cases in April and May continue to cluster within the current observed range of AH i.e. 3 to 9g/m3, then the countries experiencing monsoon, i.e. having high absolute humidity (>10 g/m3) could see a slowdown in transmissions, due to climatic factors. The data analyzed here are rapidly changing and with several unknowns including how the virus is mutating and evolving, what are the reproductive numbers and the dominant way of spreading. If 2019-nCOV is indeed sensitive to environmental factors, then it could be used to optimize the 2019-nCoV mitigation strategies. Our results in no way suggest that 2019-nCoV would not spread in warm humid regions and effective public health interventions should be implemented across the world to slow down the transmission of 2019-nCoV.

Effects of Weather on Coronavirus Pandemic.

Abstract: The novel coronavirus (SARS-CoV-2) has spread globally and has been declared a pandemic by the World Health Organization. While influenza virus shows seasonality, it is unknown if COVID-19 has any weather-related affect. In this work, we analyze the patterns in local weather of all the regions affected by COVID-19 globally. Our results indicate that approximately 85% of the COVID-19 reported cases until 1 May 2020, making approximately 3 million reported cases (out of approximately 29 million tests performed) have occurred in regions with temperature between 3 and 17 °C and absolute humidity between 1 and 9 g/m3. Similarly, hot and humid regions outside these ranges have only reported around 15% or approximately 0.5 million cases (out of approximately 7 million tests performed). This suggests that weather might be playing a role in COVID-19 spread across the world. However, this role could be limited in US and European cities (above 45 N), as mean temperature and absolute humidity levels do not reach these ranges even during the peak summer months. For hot and humid countries, most of them have already been experiencing temperatures >35 °C and absolute humidity >9 g/m3 since the beginning of March, and therefore the effect of weather, however little it is, has already been accounted for in the COVID-19 spread in those regions, and they must take strict social distancing measures to stop the further spread of COVID-19. Our analysis showed that the effect of weather may have only resulted in comparatively slower spread of COVID-19, but not halted it. We found that cases in warm and humid countries have consistently increased, accounting for approximately 500,000 cases in regions with absolute humidity >9 g/m3, therefore effective public health interventions must be implemented to stop the spread of COVID-19. This also means that 'summer' would not alone stop the spread of COVID-19 in any part of the world.

Resting State fMRI in Mice Reveals Anesthesia Specific Signatures of Brain Functional Networks and Their Interactions.

Abstract: fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al., 2014). We applied multivariate ICA analysis and Dual Regression to infer the differences in functional connectivity between isoflurane- and medetomidine-anesthetized mice. Further network analysis was performed to investigate within- and between-network connectivity differences between these anesthetic regimens. The results revealed five major networks in the mouse brain: lateral cortical, associative cortical, default mode, subcortical, and thalamic network. The anesthesia regime had a profound effect both on within- and between-network interactions. Under isoflurane anesthesia predominantly intra- and inter-cortical interactions have been observed, with only minor interactions involving subcortical structures and in particular attenuated cortico-thalamic connectivity. In contrast, medetomidine-anesthetized mice displayed subcortical functional connectivity including interactions between cortical and thalamic ICA components. Combining the two anesthetics at low dose resulted in network interaction that constituted the superposition of the interaction observed for each anesthetic alone. The study demonstrated that network modeling is a promising tool for analyzing the brain functional architecture in mice and comparing alterations therein caused by different physiological or pathological states. Understanding the differential effects of anesthetics on brain networks and their interaction is essential when interpreting fMRI data recorded under specific physiological and pathological conditions.

Increasing isoflurane dose reduces homotopic correlation and functional segregation of brain networks in mice as revealed by resting-state fMRI.

Abstract: Effects of anesthetics on brain functional networks are not fully understood. In this work, we investigated functional brain networks derived from resting-state fMRI data obtained under different doses of isoflurane in mice using stationary and dynamic functional connectivity (dFC) analysis. Stationary network analysis using FSL Nets revealed a modular structure of functional networks, which could be segregated into a lateral cortical, an associative cortical network, elements of the prefrontal network, a subcortical network, and a thalamic network. Increasing isoflurane dose led to a loss of functional connectivity between the bilateral cortical regions. In addition, dFC analysis revealed a dominance of dynamic functional states (dFS) exhibiting modular structure in mice anesthetized with a low dose of isoflurane, while at high isoflurane levels dFS showing widespread unstructured correlation displayed highest weights. This indicates that spatial segregation across brain functional networks is lost with increasing dose of the anesthetic drug used. To what extent this indicates a state of deep anesthesia remains to be shown. Combining the results of stationary and dynamic FC analysis indicates that increasing isoflurane levels leads to loss of modular network organization, which includes loss of the strong bilateral interactions between homotopic brain areas.

High Temperature and High Humidity Reduce the Transmission of COVID-19

Abstract: With the ongoing global pandemic of COVID-19, a question is whether the coming summer in the northern hemisphere will reduce the transmission intensity of COVID-19 with increased humidity and temperature. In this paper, we investigate this problem using the data from the cases with symptom-onset dates from January 19 to February 10, 2020 for 100 Chinese cities, and cases with confirmed dates from March 15 to April 25 for 1,005 U.S. counties. Statistical analysis is performed to assess the relationship between the transmissibility of COVID-19 and the temperature/humidity, by controlling for various demographic, socio-economic, geographic, healthcare and policy factors and correcting for cross-sectional correlation. We find a similar influence of the temperature and relative humidity on effective reproductive number (R values) of COVID-19 for both China and the U.S. before lockdown in both countries: one-degree Celsius increase in temperature reduces R value by about 0.023 (0.026 (95% CI [-0.0395,-0.0125]) in China and 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.), and one percent relative humidity rise reduces R value by 0.0078 (0.0076 (95% CI [-0.0108,-0.0045]) in China and 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S.). If assuming a 30 degree and 25 percent increase in temperature and relative humidity from winter to summer in the northern hemisphere, we expect the R values to decline about 0.89 (0.69 by temperature and 0.20 by humidity). Moreover, after the lockdowns in China and the U.S., temperature and relative humidity still play an important role in reducing the R values but to a less extent. Given the notion that the non-intervened R values are around 2.5 to 3, only weather factors cannot make the R values below their critical condition of R<1, under which the epidemic diminishes gradually. Therefore, public health intervention such as social distancing is crucial to block the transmission of COVID-19 even in summer.

Effects of temperature and humidity on the spread of COVID-19: A systematic review

Abstract: Background Faced with the global pandemic of COVID-19, declared by World Health Organization (WHO) on March 11th 2020, and the need to better understand the seasonal behavior of the virus, our team conducted this systematic review to describe current knowledge about the emergence and replicability of the virus and its connection with different weather factors such as temperature and relative humidity. Methods The review was registered with the PROSPERO database. The electronic databases PubMed, Scopus, Web of Science, Cochrane Library, LILACS, OpenGrey and Google Scholar were examined with the searches restricted to the years 2019 and 2020. Risk of bias assessment was performed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist tool. The GRADE tool was used to assess the certainty of the evidence. Results The initial screening identified 517 articles. After examination of the full texts, seventeen studies met the review's eligibility criteria. Great homogeneity was observed in the findings regarding the effect of temperature and humidity on the seasonal viability and transmissibility of COVID-19. Cold and dry conditions were potentiating factors on the spread of the virus. After quality assessment, two studies had a high risk of bias, eleven studies were scored as moderate risk of bias, and four studies were classified as low risk of bias. The certainty of evidence was graded as low for both outcomes evaluated. Conclusion Considering the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. However, these variables alone could not explain most of the variability in disease transmission. Therefore, the countries most affected by the disease should focus on health policies, even with climates less favorable to the virus. Although the certainty of the evidence generated was classified as low, there was homogeneity between the results reported by the included studies.

Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic.

Abstract: Preliminary evidence suggests that climate may modulate the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Yet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given that high susceptibility is a core driver. Here, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic by probing different scenarios based on known coronavirus biology. We find that although variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen, the climate drives only modest changes to pandemic size. A preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion. Our findings suggest that without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.