Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation

Round 1

Reviewer 1 Report

Comments to the article:

Using spatial patterns of COVID-19 to build a framework for economic reactivation

 

The problem presented in the article is interesting and relevant and may have practical application. However, I have comments and doubts about the results obtained from the model used by the authors.

„First: a province is vulnerable in the post-lockdown context if it has a lower incidence of poverty, unemployment, and other indicators of bad economic performance. Poverty would be inversely related to COVID-19 deaths because high economic activity, which is the counterpart of poverty, requires internal mobility and interpersonal interaction”.

And next:

„provinces that had a high level of economic activity, which is related to lower poverty and is captured in the index, are especially vulnerable against COVID-19 mortality”

It even seems logical. The poorer regions where people do not work but stay at home are less likely to be infected. Why then:

„economic activity, [..] is the counterpart of poverty?

„[..] people with a high probability to die by COVID-19, concerning young and healthy people.”

So elderly and sick people are less likely to become infected??

„Third: a province is exposed to high deprivations if fewer people have health insurance. In consequence, COVID-19 deaths could be more likely to happen. „

But in order to have health insurance one must work.

And as the authors themselves admit:

„The short-run households’ income has been drastically reduced and then slowly recovered, so there will be a shortage in the available resources to face the contagion which cannot be covered, this would lead to a greater number of deaths.”

And next:

„Provinces where the health system performs bad, are more vulnerable and likely to have a greater number of deaths.”

So, for the province to have an efficient health care system, economic activity is needed. Richer people, as it would appear from the above statement, have a better chance of survival?

And we read further that:

„Nevertheless, there is strong evidence that overcrowding produces negative exogenous interaction effects on mortality: increases in overcrowding, in neighboring provinces, can lead to lower mortality rates. „

Please explain this sentence. Where is the lower mortality rate - in poor provinces or in neighboring provinces?

„These effects are spatial interaction effects. An increase in the white population in neighboring provinces would lead to higher mortality rates in a Provence”

So, I understand that whites are more economically active?

Where do poor people live? Outside big cities? In small towns, villages? They don't live in large families in overcrowded households?

„Additionally, provinces with more overcrowding households may have worse COVID-19 outcomes„

Exactly. So, as I guess, the authors distinguish overcrowding in a given locality from overcrowding in a household, but this, it seems to me, is not explained in the article and maybe hence the misunderstanding.

„Overcrowding in households (Overcrowding): is intuitive that there is a strong positive relationship between overcrowding in households and COVID-19 mortality rates. Overcrowding is a serious issue in less developed countries, and it is a consequence of the informal settlement, bad birth control policies, among other factors.”

So poor families living in overcrowded households are at risk of infection and death?.

 

„Population density is negatively related to mortality rates, so for provinces with low population density, policymakers should ex-pect higher mortality rates,”

This, I admit, I do not understand! Or is it that poorer people are less likely to get infected, but if they do, they are more likely to die? Maybe it should also be more strongly emphasized in the article that two phenomena are investigated (obviously related to each other), but which are influenced by various factors - i.e. vulnerability to infection and mortality

 

„The rest of vulnerability drivers related to demographics, health insurance and health system perfor-mance were not significative and empirically they were not related to COVID-19 mortality rates.”

Does this not contradict the previous statements?:

„if fewer people have health insurance. In consequence, COVID-19 deaths could be more likely to hapten”

and:

„Provinces where the health system performs bad, are more vulnerable and likely to have a greater number of deaths.”

And there is also a question about this statement:

„An in-crease in the white population in neighboring provinces would lead to higher mortality rates in a province, on the other side, an increase in the black population in neighboring provinces leads to lower mortality rates in a province. „

So in those provinces where white people live there is a higher mortality, or do the neighboring provinces (where there are less white people) become infected from them? So it's better to have black neighbors than white neighbors?

„These effects are consistent with the literature and the spatial insight is useful for the management of the pandemic”.

What kind of literature is it about? I would gladly read.

Please answer these questions

And please also comment on the following statement:

„Although every level of society has been affected, the intensity of the effect has varied widely across social groups. The pandemic is impoverishing the poor and exacerbating inequality. Informal workers are severely affected by the lockdown measures.1 Low skill workers are not able to work from home. The poor and the vulnerable,2 especially those living in extreme poverty are being hit the hardest, not only in terms of lost incomes, but in terms of how their life conditions and future are threatened by this whole situation. As the virus spreads from more affluent districts where it arrived first, it affects populations that live in poorer sanitary conditions, and suffer from multiple deprivations, which are magnified due to lockdowns”.

(Source: Covid-19 and social protection of poor and vulnerable groups in Latin America: a conceptual Framework, file:///C:/Users/Dariusz%20Milewski/Downloads/undp-rblac-CD19-PDS-Number8-EN-Lustig-Tommasi.pdf)

The article seems not to have been well-reviewed before being sent for review. For example, there is such a sentence at the end:

„5. Conclusions

In this paper, we have argued that a supportive framework for economic reactivation can be built based on vulnerability against COVID-19 mortality. This vulnerability arises form-deprivation costs”

I guess it was supposed to be „from deprivation costs”?

Author Response

Dear reviewer 1

We thank you for your commentaries, they helped us to significatively improve the quality of the research article. We’ve made major revisions to the theoretical framework, results and conclusion sections. The following list details our answers to each one of the comments

Commentaries:

First reviewer

1. „First: a province is vulnerable in the post-lockdown context if it has a lower incidence of poverty, unemployment, and other indicators of bad economic performance. Poverty would be inversely related to COVID-19 deaths because high economic activity, which is the counterpart of poverty, requires internal mobility and interpersonal interaction”.

And next:

„provinces that had a high level of economic activity, which is related to lower poverty and is captured in the index, are especially vulnerable against COVID-19 mortality”

It even seems logical. The poorer regions where people do not work but stay at home are less likely to be infected. Why then:

„economic activity, [..] is the counterpart of poverty?

„[..] people with a high probability to die by COVID-19, concerning young and healthy people.”

So elderly and sick people are less likely to become infected??

„Third: a province is exposed to high deprivations if fewer people have health insurance. In consequence, COVID-19 deaths could be more likely to happen. „

But in order to have health insurance one must work.

And as the authors themselves admit:

„The short-run households’ income has been drastically reduced and then slowly recovered, so there will be a shortage in the available resources to face the contagion which cannot be covered, this would lead to a greater number of deaths.”

And next:

„Provinces where the health system performs bad, are more vulnerable and likely to have a greater number of deaths.”

So, for the province to have an efficient health care system, economic activity is needed. Richer people, as it would appear from the above statement, have a better chance of survival?

Answer:

We extended our revision of literature and improved the theoretical framework in order to dialogue with these commentaries, and relate poverty with other variables, as poverty is interacting with many other variables in the system:

Based upon the concept of deprivation costs (Holguin Veras et al., 2013) and on the literature of social determinants of health (Turner-Musa et al., 2020; Burströin & Tao, 2020; Fielding-Miller et al., 2020 and Thakur et al., 2020), we can theoretically build up a definition of vulnerability at the province level which will serve to motivate the SDMs and to define the expected signs of the effects of the variables. Our goal is to test the theoretical framework for vulnerability against COVID-19, thus some of the propositions can be empirically rejected by the evidence. This definition has seven blocks, that are built using previous literature that explores COVID-19 determinants (Andersen et al., 2021; Khalatbari-Soltani et al., 2020; Pedrosa & de Albuquerque, 2020; Rollston & Galea, 2020; Sugg et al., 2021; White & Hébert-Dufresne, 2020; Gupta et al., 2020), ML applications (Bruno et al., 2020; Carrillo-Larco & Castillo-Cara, 2020; Chakraborti et al., 2020; Rustam et al., 2020) and social determinants (Turner-Musa et al., 2020; Burströin & Tao, 2020; Fielding-Miller et al., 2020 and Thakur et al., 2020). This definition also contains the main hypothesis that will be tested in this paper.

Is important to notice that for some groups of variables, we could have two oppo-site effects according to the relationship of the variable with poverty which is the main social determinant of health: a poverty condition causes more vulnerability to COVID-19 mortality due to the scarcity of resources to face illness, malnutrition and other vulnerability conditions. Poverty in this paper will be considered as a variable that have a negative relationship with mortality rates, even if it could be positively associated to vulnerability. The reason behind this consideration is that low poverty proxies demand for help, as high economic activity provinces tend to have more cases (this argument is defended by the machine learning literature, and we acknowledge that is opposite to the social determinants argument).

Assuming that empirically poverty is negatively associated with COVID-19 mor-tality the example of health insurance makes the interpretations more complex: a good coverage of health insurance may lead to lower mortality rates, but this implies high economic activity so it may lead to higher mortality rates at the same time. The litera-ture that assesses social determinants of health has a different perspective of the ef-fects of the following definition blocks. For this literature, poverty is a condition that exacerbates COVID-19 deaths, and all other factors related to poverty such as educa-tional level, black skin-color, low income, bad system performance, among other vari-ables (Turner-Musa et al., 2020; Burströin & Tao, 2020; Fielding-Miller et al., 2020 and Thakur et al., 2020). In this paper we expect a negative relationship, because the con-text contains the data when pandemic was in its first stage, and thus is plausible that the relationship between COVID-19 and poverty is negative. The following section contains the definition blocks and the perspective bridging theories from machine learning applications and social determinants of health:

• First: a province is vulnerable in the post-lockdown context if it has a lower inci-dence of poverty, unemployment, and other indicators of bad economic perfor-mance. Economic activity, which is inversely related to poverty, requires internal mobility and interpersonal interaction (Engle et al., 2020). This does not mean that we should give less importance to poor provinces, even if they might have a lower demand for PRRSGS. The optimal policy must give the latter equal weight in the humanitarian objective function. We argue that this proposition is valid if we are measuring long-run poverty from households, short-run measures may not serve to prove this relationship.
• Second: provinces that have more proportion of the vulnerable population in terms of age (the older), sex (the males), skin color (black), chronic diseases such as hypertension, diabetes, obesity, among others. Deprivation costs are higher for people with higher probability to die by COVID-19, concerning people that has the previous characteristics (Turner-Musa et al., 2020; Burströin & Tao, 2020 and Fielding-Miller et al., 2020 reach the same signs of effects for the case of USA). Two types of overcrowding measures will be accounted for vulnerability: over-crowding in households and overcrowding in cities (proxied by population densi-ty). Provinces with higher overcrowding in households may have worse COVID-19 outcomes (Burströin & Tao, 2020). We expect the same for provinces with higher overcrowding in cities.
• Third: The short-run households’ income has been drastically reduced and then slowly recovered, so there will be a shortage in the available resources to face the contagion which cannot be covered. In the absence of high coverage of health in-surance, this would lead to a greater number of deaths. However, we acknowledge that in order to have high coverage of health insurance provinces must work, so economic activity must be high for those provinces with high health insurance coverage, and thus this could lead to higher mortality rates. In consequence, we face two possible effects of health insurance, so if health insur-ance coverage affects positively or negatively COVID-19 deaths is an empirical question.
• Fourth: health system performance indicators play a key role in the determination of COVID-19 outcomes, especially when the COVID-19 cases exceed the capacity of hospitals and other health facilities. Provinces where the health system per-forms bad, are more vulnerable and likely to have a greater number of deaths. However, as economic activity is a condition for good health system performance, the final effect is theoretically ambiguous. Whether the effect is finally positive or negative regarding COVID-19 deaths is also an empirical question.

2. And we read further that:

„Nevertheless, there is strong evidence that overcrowding produces negative exogenous interaction effects on mortality: increases in overcrowding, in neighboring provinces, can lead to lower mortality rates. „

Please explain this sentence. Where is the lower mortality rate - in poor provinces or in neighboring provinces?

Answer:

We’ve changed the results description in order to consider the interactions of poverty with other variables in the system:

Overcrowding in cities or population density (LogPD_1000): there is a strong pos-itive relationship between population density and COVID-19 mortality rates. Higher mortality is found in provinces with higher population density. In low population den-sity provinces or rural provinces, the health system may be inexistent, but since the data used for this analysis represent the initial period of COVID-19 with the limited spread between provinces we do not observe the effects of bad health system perfor-mance, but instead we observe the effect of population density or overcrowding in cit-ies where infection arrived earlier. According to empirical results, poor provinces are less overcrowded in cities (negative correlation of -0.50) but more overcrowded in households (correlation of 0.30). The effect of population density is in the same line of the effect of poverty: those provinces that are rich and has high population density (since both attributes are correlated) are more vulnerable to COVID-19 deaths. There is marginal evidence that population density produces positive exogenous interaction effects on mortality: increases in population density, in neighboring provinces, can lead to higher mortality rates, but the significance of this effect is not sufficient to re-ject the hypothesis of no effect at all. Future deeper research is needed to better explain this relationship.

Overcrowding in households (Overcrowding): is intuitive that there is a strong positive relationship between overcrowding in households and COVID-19 mortality rates. Overcrowding is a serious issue in less developed countries, and it is a conse-quence of the informal settlement, bad birth control policies, among other factors. Mortality is magnified by the fact that more individuals are living in a household than the number that is normal given the number of bedrooms. In order to improve pan-demic management, the government should minimize the contagion not only outside, but inside the household. Nevertheless, there is strong evidence that overcrowding produces negative exogenous interaction effects on mortality: increases in overcrowd-ing, in neighboring provinces, lead to lower mortality rates on a province. The indirect effect and the direct effect are counteracted, but the indirect effect is greater than the direct effect (see Table 4). The final effect of an increase in proportion of overcrowded households is a small decrease in mortality rates. Policymakers should consider that an increase in overcrowding in households may lead to lower mortality rates, even though the size of the effect is small. This is another reason why poor provinces, that are more overcrowded in households, have lower mortality rates.

3. „These effects are spatial interaction effects. An increase in the white population in neighboring provinces would lead to higher mortality rates in a Provence”

So, I understand that whites are more economically active?

Where do poor people live? Outside big cities? In small towns, villages? They don't live in large families in overcrowded households?

Answer:

We’ve included a brief explanation in theoretical framework, but also, we’ve included a change in the results section that directly respond to this commentary:

The long-run poverty (Composite_Poverty_Index) variable is related to many other variables in the model. It is negatively related to the log population density (LogPD_1000), employment (Employed_1000), human capital with secondary educa-tion (Secondary_Education_1000), log of mortality rates (Logdeaths1000), obesity prevalence (Obesity_prev_endes_1000), the average days till a patient is attended in a health facility (Days_Till_Attended), the inequity in time for patient attention (SD_Days_Till_Attended), the Asian population (Assian_1000_Inhab), and the be-longing to the coastal region (Natural_Region1), and its positively related to belonging to highlands region (Natural_Region2) and being health insured (Health_Insurance_1000). According to these correlations poor provinces are those with low population density, employment, human capital, obesity prevalence, bad performance of health system and low health insurance coverage.

4. „Additionally, provinces with more overcrowding households may have worse COVID-19 outcomes„

Exactly. So, as I guess, the authors distinguish overcrowding in a given locality from overcrowding in a household, but this, it seems to me, is not explained in the article and maybe hence the misunderstanding.

Answer:

Now it is explained in theoretical framework section:

• Second: provinces that have more proportion of the vulnerable population in terms of age (the older), sex (the males), skin color (black), chronic diseases such as hypertension, diabetes, obesity, among others. Deprivation costs are higher for people with higher probability to die by COVID-19, concerning people that has the previous characteristics (Turner-Musa et al., 2020; Burströin & Tao, 2020 and Fielding-Miller et al., 2020 reach the same signs of effects for the case of USA). Two types of overcrowding measures will be accounted for vulnerability: over-crowding in households and overcrowding in cities (proxied by population densi-ty). Provinces with higher overcrowding in households may have worse COVID-19 outcomes (Burströin & Tao, 2020). We expect the same for provinces with higher overcrowding in cities.

5. „Overcrowding in households (Overcrowding): is intuitive that there is a strong positive relationship between overcrowding in households and COVID-19 mortality rates. Overcrowding is a serious issue in less developed countries, and it is a consequence of the informal settlement, bad birth control policies, among other factors.”

So poor families living in overcrowded households are at risk of infection and death?.

Answer:

We’ve included a brief explanation in the theoretical framework, and also we report the correlations of poverty with overcrowding in results:

• Second: provinces that have more proportion of the vulnerable population in terms of age (the older), sex (the males), skin color (black), chronic diseases such as hypertension, diabetes, obesity, among others. Deprivation costs are higher for people with higher probability to die by COVID-19, concerning people that has the previous characteristics (Turner-Musa et al., 2020; Burströin & Tao, 2020 and Fielding-Miller et al., 2020 reach the same signs of effects for the case of USA). Two types of overcrowding measures will be accounted for vulnerability: over-crowding in households and overcrowding in cities (proxied by population densi-ty). Provinces with higher overcrowding in households may have worse COVID-19 outcomes (Thakur et al, 2020). We expect the same for provinces with higher overcrowding in cities. Poverty is generally negatively associated with overcrowding in cities, but positively associated with overcrowding in households.

Overcrowding in cities or population density (LogPD_1000): there is a strong pos-itive relationship between population density and COVID-19 mortality rates. Higher mortality is found in provinces with higher population density. In low population den-sity provinces or rural provinces, the health system may be inexistent, but since the data used for this analysis represent the initial period of COVID-19 with the limited spread between provinces we do not observe the effects of bad health system perfor-mance, but instead we observe the effect of population density or overcrowding in cit-ies where infection arrived earlier. According to empirical results, poor provinces are less overcrowded in cities (negative correlation of -0.50) but more overcrowded in households (correlation of 0.30). The effect of population density is in the same line of the effect of poverty: those provinces that are rich and has high population density (since both attributes are correlated) are more vulnerable to COVID-19 deaths. There is marginal evidence that population density produces positive exogenous interaction effects on mortality: increases in population density, in neighboring provinces, can lead to higher mortality rates, but the significance of this effect is not sufficient to re-ject the hypothesis of no effect at all. Future deeper research is needed to better explain this relationship.

6. „Population density is negatively related to mortality rates, so for provinces with low population density, policymakers should ex-pect higher mortality rates,”

This, I admit, I do not understand! Or is it that poorer people are less likely to get infected, but if they do, they are more likely to die? Maybe it should also be more strongly emphasized in the article that two phenomena are investigated (obviously related to each other), but which are influenced by various factors - i.e. vulnerability to infection and mortality

Answer:

The two phenomena (COVID-19 cases and death) are not independent, we are focusing on deaths rather than on cases, because in this way we prioritize demand by severity. A high number of cases without deaths is another case of severity that is beyond the scope of the paper. We’ve included a paragraph in which we present the phenomena of study in the literature review section:

The problem of study can be defined as following: there are different outcomes of COVID-19 deaths between peruvian provinces that are caused by a set of explanative factors, the main goal of this paper is to find the determinants of COVID-19 mortality rates for peruvian provinces in order to guide the disaster response and recovery at national level (Mansour et al., 2020). However, the COVID-19 deaths phenomena im-plies a strong relationship with the infection phenomena, causally infection leads to mortality, but focusing the attention on mortality rates is how we can assess severity of pandemic outbreak and guide the response and recovery.

7. „The rest of vulnerability drivers related to demographics, health insurance and health system perfor-mance were not significative and empirically they were not related to COVID-19 mortality rates.”

Does this not contradict the previous statements?:

„if fewer people have health insurance. In consequence, COVID-19 deaths could be more likely to hapten”

and:

„Provinces where the health system performs bad, are more vulnerable and likely to have a greater number of deaths.”

Answer:

The results just make it possible to accept or reject hypotheses, in this case, we reject the hypotheses. So, proposition was modified in order to capture this idea:

The principal result for the regression analysis is that SDM(1,1,1), the more general SDM is the one that better fits the data suggesting that there are endogenous, exogenous and correlated spatial interaction effects. This means that mortality rates exhibit spatial patterns. Two branches of literature were analyzed: the prediction-oriented machine learning literature and the social determinants of health literature, the evidence favors the hypotheses related to the machine learning literature for the early stage of pandemic. Poverty, population density, and overcrowding remained as the main predictors. However, overcrowding is direct and indirect (spatial spillovers) effects are counteracted, so further research is needed to clarify the relationship between overcrowding and mortality rates. Provinces are inevitably returning to their long-run level of economic activity, and high mortality rates exist because there is scarcity in essential supplies that are needed to save lives regarding COVID-19 illness. Population density is negatively related to mortality rates, so for provinces with low population density, policymakers should expect higher mortality rates, and thus delivery of PRRSGS must be prioritized. The rest of vulnerability drivers related to demographics, health insurance and health system performance were not significative and thus, empirically, we reject the hypotheses regarding these variables.

8. „These effects are consistent with the literature and the spatial insight is useful for the management of the pandemic”.

What kind of literature is it about? I would gladly read.

Answer:

The sentence has been changed; we incorporate a discussion with new literature included in the review:

Population ethnic composition (White_1000_Inhab and Black_1000_Inhab) pro-duces significant effects on mortality rates. These effects are spatial interaction effects. An increase in the white population in neighboring provinces would lead to higher mortality rates in a province, on the other side, an increase in the black population in neighboring provinces leads to lower mortality rates in a province. These effects are contrary to those effects of ethnic groups found by Thakur et al. (2020) for US. It is interesting to acknowledge that white population has higher probability to die according to the results of the model for the case of Peru.

9. And please also comment on the following statement:

„Although every level of society has been affected, the intensity of the effect has varied widely across social groups. The pandemic is impoverishing the poor and exacerbating inequality. Informal workers are severely affected by the lockdown measures.1 Low skill workers are not able to work from home. The poor and the vulnerable,2 especially those living in extreme poverty are being hit the hardest, not only in terms of lost incomes, but in terms of how their life conditions and future are threatened by this whole situation. As the virus spreads from more affluent districts where it arrived first, it affects populations that live in poorer sanitary conditions, and suffer from multiple deprivations, which are magnified due to lockdowns”.

Answer:

The relationship between pandemic outcomes and poverty is complex, as we acknowledge in the paper and in order to abord this relationship we refer to two branches of literature: the empirical literature and the social determinants of health literature. We expanded the review and improved the theoretical framework

10. The article seems not to have been well-reviewed before being sent for review. For example, there is such a sentence at the end:

„5. Conclusions

In this paper, we have argued that a supportive framework for economic reactivation can be built based on vulnerability against COVID-19 mortality. This vulnerability arises form-deprivation costs”

I guess it was supposed to be „from deprivation costs”?

Answer:

We are sorry for the mistake, it was corrected.

Author Response File: Author Response.pdf

Reviewer 2 Report

The topic examined in this thesis is very actual and interesting. The new situation created by the coronavirus epidemic raises a number of open questions that will be the source of many more studies in the future. 
The highly country-specific topic and study outlined in the thesis represent an interesting choice of subject. However, only part of the literature presented in this thesis deals with the coronavirus epidemic. I would suggest that the authors, if they have chosen a country-specific topic, should present the impact of the epidemic in more depth from several aspects, but mainly from an economic perspective. It would be good to include more national research results and sources in the literature section. The 44 sources cited could usefully be expanded further, which would be feasible on the basis of the above. 

As far as the hypotheses formulated in the thesis are concerned, the authors mention 7 of them on page 6, if I understand correctly. If these are indeed the hypotheses tested in the thesis, I would suggest that they should be much shorter, possibly in a table, while retaining the long explanation.

However, if the above are indeed the hypotheses of the study, then their proof or rejection is not shown in the essay. This should definitely be improved, making the results of the study clearly visible. 
I also do not see what software the authors used to perform each study. 
The source of figure two is also questionable, is it self-edited? The same is true for figure three. 

I would suggest that the authors make sure to cite their own results as the source under the figure or table, or perhaps next to the title, to avoid similar questions. 

In the conclusion section, it is certainly good to mention the importance of taking the research further. 

Author Response

Dear reviewer 2

We thank you for your commentaries, they helped us to significatively improve the quality of the research article. We’ve made major revisions to the theoretical framework, results and conclusion sections. The following list details our answers to each one of the comments

Second reviewer

1. However, only part of the literature presented in this thesis deals with the coronavirus epidemic. I would suggest that the authors, if they have chosen a country-specific topic, should present the impact of the epidemic in more depth from several aspects, but mainly from an economic perspective. It would be good to include more national research results and sources in the literature section. The 44 sources cited could usefully be expanded further, which would be feasible on the basis of the above. 

Answer:

We’ve included a section in literature review about the peruvian case, and added more references to the theoretical framework:

2.3. COVID-19 pandemic in Peru: a brief review

Peruvian response to pandemic was an early lockdown with a set of prohibitions and mobility restrictions. There are several ways in which pandemic affected Peruvi-ans’ livelihoods, mental health, information technology development, among others (Schwalb & Seas, 2021; Alvarez-Risco et al., 2020; Alvarez-Risco et al., 2021). Accord-ing to Schwalb & Seas (2021) Peru reached a milestone of more than 1200 deaths per one million of inhabitants by February 2021. Possible explanations for poor outcomes are overwhelmed and fragmented public health system, gaps of infrastructure, lack of specialized personnel and deficient leadership of political authorities. Following Alva-rez-Risco et al. (2020), there were and info-demic composed by excessive and un-founded information that hindered an appropriate public health response and harmed mental health of population. However, the authors argue that Peru could control the info-demic due to prison sentences applied to persons that create and share fake news. Finally, following Alvarez-Rico et al., (2021) there were good advances in digitaliza-tion that were caused by pandemic, nevertheless, more efforts are needed to imple-ment telemedicine and to offer access to the internet to achieve high-quality telemedi-cine to all the vulnerable groups in Peru.

2. As far as the hypotheses formulated in the thesis are concerned, the authors mention 7 of them on page 6, if I understand correctly. If these are indeed the hypotheses tested in the thesis, I would suggest that they should be much shorter, possibly in a table, while retaining the long explanation.

Answer:

We’ve included a table in which we summarize the expected sign of effects:

Variable	Expected sign of effect regarding mortality rates
Poverty	-
Age	+
Sex	+
Black skin-color	+
Chronic diseases	+
Overcrowding in cities	+
Overcrowding in households	+
Health insurance	?
Health system performance	?
Endogenous interaction effects	+
Exogenous interaction effects	+
Correlated effects	+

 

3. However, if the above are indeed the hypotheses of the study, then their proof or rejection is not shown in the essay. This should definitely be improved, making the results of the study clearly visible. 

Answer:

The results section of the paper has been improved, and also the conclusions sections in order to make more visible that we are accepting or rejecting hypotheses from theoretical framework:

4. I also do not see what software the authors used to perform each study. 

Answer:

Two different software were used in this study: STATA 16 for spatial regression models and Python 3.6 for elastic-net regressions and demand forecasting. Also, we used QGIS software for mapping of COVID-19 mortality rates.

5. The source of figure two is also questionable, is it self-edited? The same is true for figure three. I would suggest that the authors make sure to cite their own results as the source under the figure or table, or perhaps next to the title, to avoid similar questions.

Answer:

Figures 1, 2, 3 and 4 and Tables 4, 5, 6, 7, 8 and 9 were updated to cite our own results.

6. In the conclusion section, it is certainly good to mention the importance of taking the research further. 

Answer:

We’ve added a sentence in the conclusion to account for this commentary

Further research in the field should be encouraged in order to link practitioners with academia, this can be done by using empirical methods in humanitarian logistics research. This paper contributes to the empirical literature, but research in the field should be further expanded in order to overcome the limitations and other related studies.

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Thank you for your answer. Very interesting article. I would only advise the authors to briefly, but strongly emphasize in the conclusions, what is new (compared to previous studies) that these studies have brought.

Author Response

Dear reviewer,

We thank you for your commentaries, they helped us to significatively improve the quality of the research article. We’ve made minor revisions to the conclusions section, we’ve expanded it and we emphasized the novelty of the paper.

Commentaries:

Thank you for your answer. Very interesting article. I would only advise the authors to briefly but strongly emphasize in the conclusions, what is new (compared to previous studies) that these studies have brought.

 

Answer:

We’ve modified the conclusions section, and put this new content:

The hypothesis of provinces’ vulnerability to natural disasters (and all of its blocks) is partially accepted, there are important sources of vulnerability based on poverty, population density, and overcrowding in households. Is important to notice that poverty interacts with other variables theoretically and empirically, and, in this case, it helped to define the effect of population density and overcrowding in households. Poor provinces are more overcrowded in households, but less overcrowded in cities; they also have lower mortality rates, which means that overcrowding in households is negatively correlated with mortality rates. Overcrowding in cities or population density is positively correlated with COVID-19 mortality. These correlations include spatial interactions, which were important at the moment of determining the final effect on the mortality rates. Regarding age, sex, skin color, chronic diseases prevalence, obesity, and health system performance, we have not arrived at statistically significant results, and thus we cannot reject the null hypothesis of no relationship between mortality rates and these variables.

This paper contributes to the literature with empirical methodologies, statistical (spatial regression), and machine learning (elastic-net regression) related methods, applied to the field of humanitarian logistics. The evidence provided by this paper means new insights for a specific case of study, and spatial interaction effects represent new mechanisms through which vulnerability drivers affect COVID-19 mortality. There is an increasing demand for empirical methodologies in this field because they help to integrate academia with practitioners. Consequently, the novelty in this paper, regarding similar papers on the field, is the application of humanitarian logistics principles to data-driven analysis, using the obtained insights to build a framework for economic reactivation based on analysis of demand, and identifying those goods needed for the delivery of humanitarian aid.

Author Response File: Author Response.pdf