Jianbo Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jianbo Zhang has a total of 29 co-authors. Here are the top co-authors by number of shared publications:
- Claire Duvallet
Affiliation Biobot Analytics, Inc., Cambridge, Massachusetts, United States of America
2 shared publications - Eric J. Alm
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
2 shared publications - Fuqing Wu
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2 shared publications - Janelle Renee Thompson
Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore
2 shared publications - Katya Moniz
1:Department of Biological Engineering, Massachusetts Institute of Technology
2 shared publications - Mariana Matus
Biobot Analytics, Inc., 501 Massachusetts Avenue, Cambridge, MA 02139, USA
2 shared publications
Latest Publications
- Open Access
SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases
Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we use longitudinal wastewater analysis to track SARS-CoV-2 dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.
Open AccessSARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases
Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we use longitudinal wastewater analysis to track SARS-CoV-2 dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.
- Open Access
SARS-CoV-2 titers in wastewater are higher than expected from clinically confirmed cases
Wastewater surveillance may represent a complementary approach to measure the presence and even prevalence of infectious diseases when the capacity for clinical testing is limited. Moreover, aggregate, population-wide data can help inform modeling efforts. We tested wastewater collected at a major urban treatment facility in Massachusetts and found the presence of SARS-CoV-2 at high titers in the period from March 18 - 25 using RT-qPCR. We then confirmed the identity of the PCR product by direct DNA sequencing. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of March 25. The reason for the discrepancy is not yet clear, and until further experiments are complete, these data do not necessarily indicate that clinical estimates are incorrect. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.
Open AccessSARS-CoV-2 titers in wastewater are higher than expected from clinically confirmed cases
Wastewater surveillance may represent a complementary approach to measure the presence and even prevalence of infectious diseases when the capacity for clinical testing is limited. Moreover, aggregate, population-wide data can help inform modeling efforts. We tested wastewater collected at a major urban treatment facility in Massachusetts and found the presence of SARS-CoV-2 at high titers in the period from March 18 - 25 using RT-qPCR. We then confirmed the identity of the PCR product by direct DNA sequencing. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of March 25. The reason for the discrepancy is not yet clear, and until further experiments are complete, these data do not necessarily indicate that clinical estimates are incorrect. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.